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Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering

arXiv 2211.12640 ↗First posted 23 Nov 2022cs.LG cs.DC math.OCcited by 0

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Abstract (arXiv)

Federated learning (FL) is a popular technique for distributing machine learning (ML) across a set of edge devices. In this paper, we study fully decentralized FL, where in addition to devices conducting training locally, they carry out model aggregations via cooperative consensus formation over device-to-device (D2D) networks. We introduce asynchronous, event-triggered communications among the devices to handle settings where access to a central server is not feasible. To account for the inherent resource heterogeneity and statistical diversity challenges in FL, we define personalized communication triggering conditions at each device that weigh the change in local model parameters against the available local network resources. We theoretically recover the O(lnk / √k) convergence rate to the globally optimal model of decentralized gradient descent (DGD) methods in the setup of our methodology. We provide our convergence guarantees for the last iterates of models, under relaxed graph connectivity and data heterogeneity assumptions compared with the existing literature. To do so, we demonstrate a B-connected information flow guarantee in the presence of sporadic communications over the time-varying D2D graph. Our subsequent numerical evaluations demonstrate that our methodology obtains substantial improvements in convergence speed and/or communication savings compared to existing decentralized FL baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 60 references that are in the map. First posted on arXiv 23 Nov 2022.

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31 of 60 references.

References · 31

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2018LEAF: A Benchmark for Federated Settingscited by 780
  8. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  9. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  11. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  12. 2016Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphscited by 149
19 more
  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2022ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!cited by 134
  3. 2021Decentralized Federated Averagingcited by 114
  4. 2018Distributed Stochastic Gradient Tracking Methodscited by 106
  5. 2019Peer-to-peer Federated Learning on Graphscited by 85
  6. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  7. 2013Distributed optimization over time-varying directed graphscited by 77
  8. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  9. 2020Fast-Convergent Federated Learningcited by 61
  10. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  11. 2018Push-Pull Gradient Methods for Distributed Optimization in Networkscited by 45
  12. 2020Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networkscited by 42
  13. 2018SparCML: High-Performance Sparse Communication for Machine Learningcited by 34
  14. 2020Network-Aware Optimization of Distributed Learning for Fog Computingcited by 27
  15. 2018A linear algorithm for optimization over directed graphs with geometric convergencecited by 25
  16. 2022On the Performance of Gradient Tracking with Local Updatescited by 21
  17. 2018Asynchronous Gradient-Pushcited by 20
  18. 2021Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneitycited by 12
  19. 2022Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholdscited by 9

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