FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

Applying federated learning (FL) on Internet of Things (IoT) devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL efficient: 1) execution on devices with limited computational capabilities; 2) accounting for stragglers due to computational heterogeneity of devices; and 3) adaptation to the changing network bandwidths. This article presents FedAdapt, an adaptive offloading FL framework to mitigate the aforementioned challenges. FedAdapt accelerates local training in computationally constrained devices by leveraging layer offloading of deep neural networks (DNNs) to servers. Furthermore, FedAdapt adopts reinforcement learning (RL)-based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth. The experimental studies are carried out on a lab-based testbed and it is demonstrated that by offloading a DNN from the device to the server FedAdapt reduces the training time of a typical IoT device by over half compared to classic FL. The training time of extreme stragglers and the overall training time can be reduced by up to 57%. Furthermore, with changing network bandwidth, FedAdapt is demonstrated to reduce the training time by up to 40% when compared to classic FL, without sacrificing accuracy.

[1]  Seyit Camtepe,et al.  SplitFed: When Federated Learning Meets Split Learning , 2020, AAAI.

[2]  Jinjun Xiong,et al.  Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration , 2021, 2021 58th ACM/IEEE Design Automation Conference (DAC).

[3]  Chaoyang He,et al.  Federated Learning for Internet of Things , 2021, SenSys.

[4]  Yuanyuan Yang,et al.  Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity , 2020, IEEE Transactions on Parallel and Distributed Systems.

[5]  Richard Nock,et al.  Advances and Open Problems in Federated Learning , 2019, Found. Trends Mach. Learn..

[6]  Dong-Jun Han,et al.  Accelerating Federated Learning with Split Learning on Locally Generated Losses , 2021 .

[7]  Blesson Varghese,et al.  Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks , 2020, 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC).

[8]  Jilin Zhang,et al.  A Survey of Computation Offloading in Edge Computing , 2020, 2020 International Conference on Computer, Information and Telecommunication Systems (CITS).

[9]  Shuai Yi,et al.  Performance Optimization of Federated Person Re-identification via Benchmark Analysis , 2020, ACM Multimedia.

[10]  Murali Annavaram,et al.  Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge , 2020, NeurIPS.

[11]  WangLizhe,et al.  Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey , 2020 .

[12]  Christopher Briggs,et al.  A Review of Privacy Preserving Federated Learning for Private IoT Analytics , 2020, ArXiv.

[13]  Surya Nepal,et al.  End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things , 2020, 2020 International Symposium on Reliable Distributed Systems (SRDS).

[14]  M. Hadi Amini,et al.  Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art , 2020, ArXiv.

[15]  Martin Slawski,et al.  Asynchronous Online Federated Learning for Edge Devices with Non-IID Data , 2019, 2020 IEEE International Conference on Big Data (Big Data).

[16]  Xin Qin,et al.  FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare , 2019, IEEE Intelligent Systems.

[17]  Nageen Himayat,et al.  Coded Federated Learning , 2019, 2019 IEEE Globecom Workshops (GC Wkshps).

[18]  Jun Wang,et al.  SmartPC: Hierarchical Pace Control in Real-Time Federated Learning System , 2019, 2019 IEEE Real-Time Systems Symposium (RTSS).

[19]  T. Brunschwiler,et al.  Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices , 2019, Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things.

[20]  Thar Baker,et al.  A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing , 2019, IEEE Access.

[21]  Assaf Schuster,et al.  Taming Momentum in a Distributed Asynchronous Environment , 2019, ArXiv.

[22]  Xu Chen,et al.  Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing , 2019, Proceedings of the IEEE.

[23]  Tim Kraska,et al.  Park: An Open Platform for Learning-Augmented Computer Systems , 2019, NeurIPS.

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

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

[26]  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).

[27]  Ramesh Raskar,et al.  Split learning for health: Distributed deep learning without sharing raw patient data , 2018, ArXiv.

[28]  Xu Chen,et al.  Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy , 2018, MECOMM@SIGCOMM.

[29]  Ali Kashif Bashir,et al.  A Survey on Resource Management in IoT Operating Systems , 2018, IEEE Access.

[30]  Hongzi Mao,et al.  Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.

[31]  Alec Radford,et al.  Proximal Policy Optimization Algorithms , 2017, ArXiv.

[32]  Akanksha Rai Sharma,et al.  Literature survey of statistical, deep and reinforcement learning in natural language processing , 2017, 2017 International Conference on Computing, Communication and Automation (ICCCA).

[33]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[34]  Etienne Perot,et al.  Deep Reinforcement Learning framework for Autonomous Driving , 2017, Autonomous Vehicles and Machines.

[35]  Trevor N. Mudge,et al.  Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge , 2017, ASPLOS.

[36]  Sergey Levine,et al.  Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

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

[38]  Benjamin Van Roy,et al.  Deep Exploration via Bootstrapped DQN , 2016, NIPS.

[39]  Albert Y. Zomaya,et al.  Big Data Privacy in the Internet of Things Era , 2014, IT Professional.

[40]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[41]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[42]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[43]  Hui Lin,et al.  Image-based seat belt detection , 2011, Proceedings of 2011 IEEE International Conference on Vehicular Electronics and Safety.

[44]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[45]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.