Wireless Time-Triggered Federated Learning with Adaptive Local Training Optimization

Xiaokang Zhou, Yansha Deng, Huiyun Xia, Shaochuan Wu

Research output: Contribution to conference typesPaperpeer-review

Abstract

Traditional synchronous and asynchronous federated learning suffer from limitations, such as straggler, high communication overhead, and model staleness problems. As a generalization of the former two, time-triggered federated learning (TT-Fed) offers a new perspective to alleviate these problems and provides better trade-off between communication and training efficiencies. In this paper, we consider the dynamic aggregation frequency optimization problem under constrained system resources for TT-Fed. We analyze how the number of local training epochs affects the performance of TT-Fed and quantify the impacts of distributed data heterogeneity and model staleness heterogeneity to its convergence upper bound. Based on the derived upper bound, we propose an adaptive local training optimization algorithm to minimize the system loss under constrained resource budget. Numerical simulations show that compared to TT-Fed with fixed number of local training epochs, our proposed adaptive optimization algorithm can provide near optimal results under different degrees of non-IID data distributions.

Original languageEnglish
DOIs
Publication statusPublished - 2023
Event2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023 - Hoboken, United States
Duration: 20 May 2023 → …

Conference

Conference2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023
Country/TerritoryUnited States
CityHoboken
Period20/05/2023 → …

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