A Systematic Literature Review on Federated Machine Learning

Federated learning is an emerging machine learning paradigm where multiple clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning from a software engineering perspective, we performed a systematic literature review with the extracted 231 primary studies. The results show that most of the known motivations of federated learning appear to be the most studied federated learning challenges, such as communication efficiency and statistical heterogeneity. Also, there are only a few real-world applications of federated learning. Hence, more studies in this area are needed before the actual industrial-level adoption of federated learning.

[1]  Leandros Tassiulas,et al.  Model Pruning Enables Efficient Federated Learning on Edge Devices , 2019, ArXiv.

[2]  Jeffrey Li,et al.  Differentially Private Meta-Learning , 2020, ICLR.

[3]  Anit Kumar Sahu,et al.  Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.

[4]  Boi Faltings,et al.  Federated Learning with Bayesian Differential Privacy , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[5]  Shengli Xie,et al.  Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory , 2019, IEEE Internet of Things Journal.

[6]  Hubert Eichner,et al.  APPLIED FEDERATED LEARNING: IMPROVING GOOGLE KEYBOARD QUERY SUGGESTIONS , 2018, ArXiv.

[7]  Tianjian Chen,et al.  Federated Machine Learning: Concept and Applications , 2019 .

[8]  Shengwen Yang,et al.  Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator , 2019, ArXiv.

[9]  Gail C. Murphy,et al.  How does Machine Learning Change Software Development Practices? , 2021, IEEE Transactions on Software Engineering.

[10]  Jinho Choi,et al.  Federated Learning With Multichannel ALOHA , 2020, IEEE Wireless Communications Letters.

[11]  Qiang Yang,et al.  A Communication Efficient Vertical Federated Learning Framework , 2019, ArXiv.

[12]  Indranil Gupta,et al.  Asynchronous Federated Optimization , 2019, ArXiv.

[13]  Zhiwei Steven Wu,et al.  Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms , 2019, NeurIPS.

[14]  H. Vincent Poor,et al.  Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks , 2019, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Moming Duan,et al.  Astraea: Self-Balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications , 2019, 2019 IEEE 37th International Conference on Computer Design (ICCD).

[16]  Virendra J. Marathe,et al.  Private Federated Learning with Domain Adaptation , 2019, ArXiv.

[17]  Pan Zhou,et al.  A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems , 2019, IEEE Transactions on Knowledge and Data Engineering.

[18]  H. Vincent Poor,et al.  Update Aware Device Scheduling for Federated Learning at the Wireless Edge , 2020, 2020 IEEE International Symposium on Information Theory (ISIT).

[19]  Monica Nicoli,et al.  Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks , 2019, IEEE Internet of Things Journal.

[20]  Seong Joon Oh,et al.  Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning , 2018, 1805.05838.

[21]  Hubert Eichner,et al.  Towards Federated Learning at Scale: System Design , 2019, SysML.

[22]  H. Vincent Poor,et al.  Federated Learning With Differential Privacy: Algorithms and Performance Analysis , 2019, IEEE Transactions on Information Forensics and Security.

[23]  Athina Markopoulou,et al.  A Federated Learning Approach for Mobile Packet Classification , 2019, ArXiv.

[24]  Anusha Lalitha,et al.  Fully Decentralized Federated Learning , 2018 .

[25]  Jakub Konecný,et al.  Federated Learning with Autotuned Communication-Efficient Secure Aggregation , 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.

[26]  Ming Liu,et al.  Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems , 2019, IEEE Robotics and Automation Letters.

[27]  H. Vincent Poor,et al.  Scheduling Policies for Federated Learning in Wireless Networks , 2019, IEEE Transactions on Communications.

[28]  Abdelhakim Senhaji Hafid,et al.  Record and Reward Federated Learning Contributions with Blockchain , 2019, 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC).

[29]  Ameet S. Talwalkar,et al.  Federated Kernelized Multi-Task Learning , 2018 .

[30]  Yuanming Shi,et al.  On-Device Federated Learning via Second-Order Optimization with Over-the-Air Computation , 2019, 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).

[31]  Vladimir Vlassov,et al.  Human Activity Recognition Using Federated Learning , 2018, 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom).

[32]  Tommaso Di Noia,et al.  Towards Effective Device-Aware Federated Learning , 2019, AI*IA.

[33]  Choong Seon Hong,et al.  Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance , 2019, 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[34]  Chetan Nadiger,et al.  Federated Reinforcement Learning for Fast Personalization , 2019, 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).

[35]  Yan Zhang,et al.  Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.

[36]  Kin K. Leung,et al.  Energy-Efficient Radio Resource Allocation for Federated Edge Learning , 2019, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[37]  Joachim M. Buhmann,et al.  Variational Federated Multi-Task Learning , 2019, ArXiv.

[38]  Tian Li,et al.  Fair Resource Allocation in Federated Learning , 2019, ICLR.

[39]  Qiang Yang,et al.  SecureBoost: A Lossless Federated Learning Framework , 2019, IEEE Intelligent Systems.

[40]  Kan Yang,et al.  VerifyNet: Secure and Verifiable Federated Learning , 2020, IEEE Transactions on Information Forensics and Security.

[41]  Stephen A. Jarvis,et al.  SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead , 2019, IEEE Transactions on Computers.

[42]  Hamed Haddadi,et al.  Efficient and Private Federated Learning using TEE , 2019 .

[43]  Aryan Mokhtari,et al.  FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization , 2019, AISTATS.

[44]  Ameet Talwalkar,et al.  One-Shot Federated Learning , 2019, ArXiv.

[45]  Choong Seon Hong,et al.  FLchain: Federated Learning via MEC-enabled Blockchain Network , 2019, 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[46]  Spyridon Bakas,et al.  Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation , 2018, BrainLes@MICCAI.

[47]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[48]  Yulei Wu,et al.  FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things , 2020, IEEE Internet of Things Journal.

[49]  Mats Jirstrand,et al.  Functional Federated Learning in Erlang (ffl-erl) , 2018, WFLP.

[50]  Tzu-Ming Harry Hsu,et al.  Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification , 2019, ArXiv.

[51]  Yasaman Khazaeni,et al.  Bayesian Nonparametric Federated Learning of Neural Networks , 2019, ICML.

[52]  Deniz Gündüz,et al.  Federated Learning Over Wireless Fading Channels , 2019, IEEE Transactions on Wireless Communications.

[53]  Seong-Lyun Kim,et al.  Blockchained On-Device Federated Learning , 2018, IEEE Communications Letters.

[54]  J. Murphy.  The General Data Protection Regulation (GDPR) , 2018, Irish medical journal.

[55]  Lili Su,et al.  Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent , 2017, Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems.

[56]  Sarvar Patel,et al.  Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..

[57]  Di Wu,et al.  Multi-Task Network Anomaly Detection using Federated Learning , 2019, SoICT.

[58]  Ying-Chang Liang,et al.  Federated Learning in Mobile Edge Networks: A Comprehensive Survey , 2020, IEEE Communications Surveys & Tutorials.

[59]  Bernd Bischl,et al.  High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions , 2019, IntelliSys.

[60]  Yaochu Jin,et al.  Multi-Objective Evolutionary Federated Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[61]  Wojciech Samek,et al.  Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[62]  Yan Zhang,et al.  Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics , 2020, IEEE Transactions on Industrial Informatics.

[63]  Roel Wieringa,et al.  Requirements engineering paper classification and evaluation criteria: a proposal and a discussion , 2005, Requirements Engineering.

[64]  Hui Li,et al.  Blockchain-Based Privacy Preserving Deep Learning , 2018, Inscrypt.

[65]  Matthew Deaves,et al.  General Data Protection Regulation (GDPR) , 2017 .

[66]  Huzefa Rangwala,et al.  Asynchronous Online Federated Learning for Edge Devices , 2019, ArXiv.

[67]  Zhisheng Niu,et al.  Device Scheduling with Fast Convergence for Wireless Federated Learning , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[68]  Ying-Chang Liang,et al.  Joint Service Pricing and Cooperative Relay Communication for Federated Learning , 2018, 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[69]  Yang Liu,et al.  Secure Federated Transfer Learning , 2018, ArXiv.

[70]  Tara Javidi,et al.  Decentralized Bayesian Learning over Graphs , 2019, ArXiv.

[71]  Deniz Gündüz,et al.  Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks , 2019, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[72]  Tianjian Chen,et al.  Abnormal Client Behavior Detection in Federated Learning , 2019, ArXiv.

[73]  Jun Zhao,et al.  Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System , 2019, ArXiv.

[74]  Mohammad Eshghi,et al.  Privacy Preserved Decentralized Deep Learning: A Blockchain Based Solution for Secure AI-Driven Enterprise , 2019 .

[75]  Tracy Hall,et al.  A Systematic Literature Review on Fault Prediction Performance in Software Engineering , 2012, IEEE Transactions on Software Engineering.

[76]  Richard Nock,et al.  Advances and Open Problems in Federated Learning , 2021, Found. Trends Mach. Learn..

[77]  Mehdi Bennis,et al.  Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data , 2018, ArXiv.

[78]  Xiao Zeng,et al.  Efficient Federated Learning via Variational Dropout , 2018 .

[79]  Wouter Joosen,et al.  Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study , 2018, Applied Sciences.

[80]  Mehryar Mohri,et al.  Agnostic Federated Learning , 2019, ICML.

[81]  Runhua Xu,et al.  HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning , 2019, AISec@CCS.

[82]  Swaroop Ramaswamy,et al.  Federated Learning for Emoji Prediction in a Mobile Keyboard , 2019, ArXiv.

[83]  Lili Su,et al.  Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent , 2019, PERV.

[84]  Sarvar Patel,et al.  Practical Secure Aggregation for Federated Learning on User-Held Data , 2016, ArXiv.

[85]  Qian Xu,et al.  Federated Topic Modeling , 2019, CIKM.

[86]  H. Brendan McMahan,et al.  Generative Models for Effective ML on Private, Decentralized Datasets , 2019, ICLR.

[87]  Yue Zhao,et al.  Federated Learning with Non-IID Data , 2018, ArXiv.

[88]  Jingyan Jiang,et al.  Decentralized Federated Learning: A Segmented Gossip Approach , 2019, ArXiv.

[89]  Lifeng Sun,et al.  Towards Faster and Better Federated Learning: A Feature Fusion Approach , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[90]  Kai Chen,et al.  Secure Federated Matrix Factorization , 2019, IEEE Intelligent Systems.

[91]  Shuai Zheng,et al.  Federated Learning-Based Computation Offloading Optimization in Edge Computing-Supported Internet of Things , 2019, IEEE Access.

[92]  ChenYudong,et al.  Distributed Statistical Machine Learning in Adversarial Settings , 2017 .

[93]  Song Guo,et al.  Experience-Driven Computational Resource Allocation of Federated Learning by Deep Reinforcement Learning , 2020, 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[94]  Hubert Eichner,et al.  Federated Learning for Mobile Keyboard Prediction , 2018, ArXiv.

[95]  Solmaz Niknam,et al.  Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges , 2019, IEEE Communications Magazine.

[96]  Yan Wang,et al.  Computation Offloading with Multiple Agents in Edge-Computing–Supported IoT , 2019, ACM Trans. Sens. Networks.

[97]  Sashank J. Reddi,et al.  SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning , 2019, ArXiv.

[98]  Mugen Peng,et al.  Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications , 2020, IEEE Internet of Things Journal.

[99]  Jun Zhang,et al.  Edge-Assisted Hierarchical Federated Learning with Non-IID Data , 2019, ArXiv.

[100]  Antti Honkela,et al.  Learning rate adaptation for federated and differentially private learning , 2018, 1809.03832.

[101]  Sreeram Kannan,et al.  Improving Federated Learning Personalization via Model Agnostic Meta Learning , 2019, ArXiv.

[102]  Bingsheng He,et al.  A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection , 2019, IEEE Transactions on Knowledge and Data Engineering.

[103]  Lars Kai Hansen,et al.  Active Learning Solution on Distributed Edge Computing , 2019, ArXiv.

[104]  Boi Faltings,et al.  Federated Generative Privacy , 2019, IEEE Intelligent Systems.

[105]  Sebastian Caldas,et al.  LEAF: A Benchmark for Federated Settings , 2018, ArXiv.

[106]  Dinesh C. Verma,et al.  Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption , 2018, PADG@ESORICS.

[107]  Kaibin Huang,et al.  Broadband Analog Aggregation for Low-Latency Federated Edge Learning , 2018, IEEE Transactions on Wireless Communications.

[108]  Nassir Navab,et al.  BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning , 2019, ArXiv.

[109]  Alan M. Davis,et al.  Good requirements practices are neither necessary nor sufficient , 2005, Requirements Engineering.

[110]  Fanny Andalia,et al.  Implementation of Analytical Hierarchy Process On Airplane Ticket Booking Application Selection With Software Quality Requirements and Evaluation ISO / IEC 25010 : 2011 , 2018 .

[111]  Xiaoyan Sun,et al.  Model and Feature Aggregation Based Federated Learning for Multi-sensor Time Series Trend Following , 2019, IWANN.

[112]  Ivan Beschastnikh,et al.  Mitigating Sybils in Federated Learning Poisoning , 2018, ArXiv.

[113]  Qing Ling,et al.  Byzantine-Robust Stochastic Gradient Descent for Distributed Low-Rank Matrix Completion , 2019, 2019 IEEE Data Science Workshop (DSW).

[114]  Geyong Min,et al.  Federated Learning Based Proactive Content Caching in Edge Computing , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[115]  Li Huang,et al.  Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records , 2019, J. Biomed. Informatics.

[116]  Wei Wang,et al.  CMFL: Mitigating Communication Overhead for Federated Learning , 2019, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).

[117]  Tianjian Chen,et al.  Multi-Agent Visualization for Explaining Federated Learning , 2019, IJCAI.

[118]  Walid Saad,et al.  Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism , 2019, IEEE Communications Magazine.

[119]  Fei Chen,et al.  Federated Meta-Learning with Fast Convergence and Efficient Communication , 2018 .

[120]  Tianjian Chen,et al.  A Secure Federated Transfer Learning Framework , 2020, IEEE Intelligent Systems.

[121]  Richard Nock,et al.  Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption , 2017, ArXiv.

[122]  Weishan Zhang,et al.  Dynamic-Fusion-Based Federated Learning for COVID-19 Detection , 2020, IEEE Internet of Things Journal.

[123]  Klaus-Robert Müller,et al.  Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[124]  Joseph Dureau,et al.  Federated Learning for Keyword Spotting , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[125]  Hien Quoc Ngo,et al.  Cell-Free Massive MIMO for Wireless Federated Learning , 2019, IEEE Transactions on Wireless Communications.

[126]  Miao Pan,et al.  Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach , 2020, IEEE Access.

[127]  Guanding Yu,et al.  Accelerating DNN Training in Wireless Federated Edge Learning Systems , 2019, IEEE Journal on Selected Areas in Communications.

[128]  Fengjun Li,et al.  Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain , 2019, CCS.

[129]  Anit Kumar Sahu,et al.  Federated Optimization in Heterogeneous Networks , 2018, MLSys.

[130]  Daniel Benditkis,et al.  Distributed deep neural network training on edge devices , 2019, SEC.

[131]  Yang Liu,et al.  Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing , 2019, ArXiv.

[132]  Gaurav Kapoor,et al.  Protection Against Reconstruction and Its Applications in Private Federated Learning , 2018, ArXiv.

[133]  Ivan Beschastnikh,et al.  Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning , 2018, ArXiv.

[134]  Badih Ghazi,et al.  Scalable and Differentially Private Distributed Aggregation in the Shuffled Model , 2019, ArXiv.

[135]  Mohsen Guizani,et al.  Reliable Federated Learning for Mobile Networks , 2019, IEEE Wireless Communications.

[136]  Ryosuke Shibasaki,et al.  Decentralized Attention-based Personalized Human Mobility Prediction , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[137]  Sebastian Caldas,et al.  Expanding the Reach of Federated Learning by Reducing Client Resource Requirements , 2018, ArXiv.

[138]  Han Yu,et al.  Threats to Federated Learning: A Survey , 2020, ArXiv.

[139]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[140]  Yao Zheng,et al.  FaIR: Federated Incumbent Detection in CBRS Band , 2019, 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[141]  Peter Richtárik,et al.  Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.

[142]  Xiaoyan Sun,et al.  Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[143]  Sashank J. Reddi,et al.  SCAFFOLD: Stochastic Controlled Averaging for Federated Learning , 2019, ICML.

[144]  Sébastien Gambs,et al.  IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning , 2019, 2019 IEEE Security and Privacy Workshops (SPW).

[145]  Yanchao Zhao,et al.  An Efficient Federated Learning Scheme with Differential Privacy in Mobile Edge Computing , 2019, MLICOM.

[146]  Di Wu,et al.  PDGAN: A Novel Poisoning Defense Method in Federated Learning Using Generative Adversarial Network , 2019, ICA3PP.

[147]  Yaoxue Zhang,et al.  JointRec: A Deep-Learning-Based Joint Cloud Video Recommendation Framework for Mobile IoT , 2020, IEEE Internet of Things Journal.

[148]  Xiaoyan Sun,et al.  Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[149]  H. Vincent Poor,et al.  Performance Optimization of Federated Learning over Wireless Networks , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[150]  Xiangliang Zhang,et al.  Robust Federated Training via Collaborative Machine Teaching using Trusted Instances , 2019, ArXiv.

[151]  Ivan Beschastnikh,et al.  Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting , 2018, ArXiv.

[152]  Zhi Ding,et al.  Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.

[153]  Tianqing Zhu,et al.  Detecting Suicidal Ideation with Data Protection in Online Communities , 2019, DASFAA.

[154]  Jie Xu,et al.  Federated Learning for Healthcare Informatics , 2019, ArXiv.

[155]  Takayuki Nishio,et al.  Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[156]  Joseph K. Liu,et al.  DeepPAR and DeepDPA: Privacy Preserving and Asynchronous Deep Learning for Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.

[157]  Deniz Gündüz,et al.  Energy-Aware Analog Aggregation for Federated Learning with Redundant Data , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[158]  Hongyu Li,et al.  An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning , 2019, 2019 Data Compression Conference (DCC).

[159]  Yunus Sarikaya,et al.  Motivating Workers in Federated Learning: A Stackelberg Game Perspective , 2019, IEEE Networking Letters.

[160]  Canh Dinh,et al.  Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation , 2019, IEEE/ACM Transactions on Networking.

[161]  Walid Saad,et al.  Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks , 2018, IEEE Transactions on Wireless Communications.

[162]  Zaïd Harchaoui,et al.  Robust Aggregation for Federated Learning , 2019, IEEE Transactions on Signal Processing.

[163]  Ada Gavrilovska,et al.  Cartel: A System for Collaborative Transfer Learning at the Edge , 2019, SoCC.

[164]  Daniel Rueckert,et al.  A generic framework for privacy preserving deep learning , 2018, ArXiv.

[165]  Manoj A. Thomas,et al.  Federated Machine Learning for Translational Research , 2018, AMCIS.

[166]  Nguyen H. Tran,et al.  Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network , 2020, IEEE Access.

[167]  Hubert Eichner,et al.  Federated Evaluation of On-device Personalization , 2019, ArXiv.

[168]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[169]  Lars Kai Hansen,et al.  Distributed Active Learning Strategies on Edge Computing , 2019, 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom).

[170]  Aris Gkoulalas-Divanis,et al.  Differential Privacy-enabled Federated Learning for Sensitive Health Data , 2019, ArXiv.

[171]  Kejiang Ye,et al.  FFD: A Federated Learning Based Method for Credit Card Fraud Detection , 2019, BigData.

[172]  Dusit Niyato,et al.  Mobile Device Training Strategies in Federated Learning: An Evolutionary Game Approach , 2019, 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[173]  Lifeng Sun,et al.  Two-Stream Federated Learning: Reduce the Communication Costs , 2018, 2018 IEEE Visual Communications and Image Processing (VCIP).

[174]  Samuel Marchal,et al.  DÏoT: A Federated Self-learning Anomaly Detection System for IoT , 2018, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).

[175]  Chen Gong,et al.  Content Compression Coding for Federated Learning , 2019, 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP).

[176]  Junpu Wang,et al.  FedMD: Heterogenous Federated Learning via Model Distillation , 2019, ArXiv.

[177]  Guan Wang,et al.  Interpret Federated Learning with Shapley Values , 2019, ArXiv.

[178]  Axel Legay,et al.  Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn) , 2019, IEEE Access.

[179]  Dinesh C. Verma,et al.  Approaches to address the data skew problem in federated learning , 2019, Defense + Commercial Sensing.

[180]  Kannan Ramchandran,et al.  Robust Federated Learning in a Heterogeneous Environment , 2019, ArXiv.

[181]  Huadong Ma,et al.  Federated Region-Learning: An Edge Computing Based Framework for Urban Environment Sensing , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[182]  Ying-Chang Liang,et al.  Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach , 2019, 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS).

[183]  Tassilo Klein,et al.  Differentially Private Federated Learning: A Client Level Perspective , 2017, ArXiv.

[184]  Yuanming Shi,et al.  A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression , 2019, ArXiv.

[185]  Zhu Han,et al.  Incentivize to Build: A Crowdsourcing Framework for Federated Learning , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[186]  Jing Ma,et al.  Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis , 2019, CIKM.

[187]  Haomiao Yang,et al.  Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence , 2020, IEEE Transactions on Industrial Informatics.

[188]  Yue Zhang,et al.  DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive , 2019, IEEE Transactions on Dependable and Secure Computing.

[189]  Geyong Min,et al.  Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT , 2020, IEEE Internet of Things Journal.

[190]  Choong Seon Hong,et al.  A Crowdsourcing Framework for On-Device Federated Learning , 2020, IEEE Transactions on Wireless Communications.

[191]  Long Hu,et al.  Privacy-aware service placement for mobile edge computing via federated learning , 2019, Inf. Sci..

[192]  Marco Scavuzzo,et al.  A Simple and Efficient Federated Recommender System , 2019, BDCAT.

[193]  Tianjian Chen,et al.  A Communication Efficient Collaborative Learning Framework for Distributed Features , 2019 .

[194]  Tara Javidi,et al.  Peer-to-peer Federated Learning on Graphs , 2019, ArXiv.

[195]  Walid Saad,et al.  A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.

[196]  Liming Zhu,et al.  Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT , 2021, IEEE Internet of Things Journal.

[197]  Haomiao Yang,et al.  Towards Efficient and Privacy-Preserving Federated Deep Learning , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[198]  Bo Ding,et al.  Real-Time Data Processing Architecture for Multi-Robots Based on Differential Federated Learning , 2018, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[199]  Deze Zeng,et al.  A Learning-Based Incentive Mechanism for Federated Learning , 2020, IEEE Internet of Things Journal.

[200]  Paul M. Thompson,et al.  Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[201]  Dinesh C. Verma,et al.  Federated AI for the Enterprise: A Web Services Based Implementation , 2019, 2019 IEEE International Conference on Web Services (ICWS).

[202]  Kalikinkar Mandal,et al.  PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks , 2019, IACR Cryptol. ePrint Arch..

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