Over-the-Air Decentralized Federated Learning
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[1] Jie Xu,et al. D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge , 2020, IEEE Wireless Communications Letters.
[2] Deniz Gündüz,et al. Decentralized SGD with Over-the-Air Computation , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.
[3] Richard Nock,et al. Advances and Open Problems in Federated Learning , 2021, Found. Trends Mach. Learn..
[4] Liangzhong Ruan,et al. Interference Alignment for Partially Connected MIMO Cellular Networks , 2012, IEEE Transactions on Signal Processing.
[5] Angelia Nedic,et al. Distributed stochastic gradient tracking methods , 2018, Mathematical Programming.
[6] Yong Zhou,et al. Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface , 2020, IEEE Network.
[7] Stephen P. Boyd,et al. Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).
[8] Zhibin Wang,et al. Wireless-Powered Over-the-Air Computation in Intelligent Reflecting Surface-Aided IoT Networks , 2021, IEEE Internet of Things Journal.
[9] Syed Ali Jafar,et al. Topological Interference Management Through Index Coding , 2013, IEEE Transactions on Information Theory.
[10] Yuanming Shi,et al. Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory , 2021, IEEE Journal on Selected Areas in Communications.
[11] Zhi Ding,et al. Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow , 2018, IEEE Transactions on Signal Processing.
[12] U. Khan,et al. Variance-Reduced Decentralized Stochastic Optimization With Accelerated Convergence , 2019, IEEE Transactions on Signal Processing.
[13] Zhi Ding,et al. Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.
[14] Ying-Chang Liang,et al. Federated Learning in Mobile Edge Networks: A Comprehensive Survey , 2020, IEEE Communications Surveys & Tutorials.
[15] Kobi Cohen,et al. On Analog Gradient Descent Learning Over Multiple Access Fading Channels , 2019, IEEE Transactions on Signal Processing.
[16] Deniz Gündüz,et al. Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).
[17] Zhibin Wang,et al. Federated Learning via Intelligent Reflecting Surface , 2020, IEEE Transactions on Wireless Communications.
[18] Wei Chen,et al. The Roadmap to 6G: AI Empowered Wireless Networks , 2019, IEEE Communications Magazine.
[19] Halim Yanikomeroglu,et al. Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions , 2014, IEEE Communications Magazine.
[20] B. Reed. Graph Colouring and the Probabilistic Method , 2001 .
[21] Solmaz Niknam,et al. Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges , 2019, IEEE Communications Magazine.
[22] Wei Zhang,et al. Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent , 2017, NIPS.
[23] M. Bennis,et al. Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning , 2020, IEEE Transactions on Wireless Communications.
[24] H. Vincent Poor,et al. Convergence Time Optimization for Federated Learning Over Wireless Networks , 2020, IEEE Transactions on Wireless Communications.
[25] Martin Jaggi,et al. A Unified Theory of Decentralized SGD with Changing Topology and Local Updates , 2020, ICML.
[26] Soummya Kar,et al. Decentralized Stochastic Optimization and Machine Learning: A Unified Variance-Reduction Framework for Robust Performance and Fast Convergence , 2020, IEEE Signal Processing Magazine.
[27] Yan Zhang,et al. Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.
[28] Jun Zhang,et al. Communication-Efficient Edge AI: Algorithms and Systems , 2020, IEEE Communications Surveys & Tutorials.
[29] Walid Saad,et al. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.
[30] Mehdi Bennis,et al. Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems , 2021, IEEE Communications Magazine.
[31] Mohsen Guizani,et al. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond , 2021, IEEE Internet of Things Journal.
[32] Shuguang Cui,et al. Federated Learning for 6G: Applications, Challenges, and Opportunities , 2021, Engineering.
[33] Kaibin Huang,et al. Over-the-Air Computing for 6G - Turning Air into a Computer , 2020, ArXiv.
[34] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[35] Mehdi Bennis,et al. A Joint Decentralized Federated Learning and Communications Framework for Industrial Networks , 2020, 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).
[36] Na Li,et al. Harnessing smoothness to accelerate distributed optimization , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).
[37] Osvaldo Simeone,et al. Decentralized Federated Learning via SGD over Wireless D2D Networks , 2020, 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).
[38] Praveen Kumar Reddy Maddikunta,et al. Fusion of Federated Learning and Industrial Internet of Things: A Survey , 2021, Comput. Networks.