Optimal Batch Allocation for Wireless Federated Learning
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Abstract (arXiv)
Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: time-division multiple access (TDMA) and random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the non-zero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 3 Apr 2024.
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23 of 41 references.
References · 23
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2021Convex optimizationcited by 2,479
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
- 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
- 2018LEAF: A Benchmark for Federated Settingscited by 780
- 2018Local SGD Converges Fast and Communicates Littlecited by 595
- 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
- 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
- 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
- 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
- 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
11 more
- 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
- 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
- 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
- 2017Communication-Efficient Algorithms for Decentralized and Stochastic Optimizationcited by 42
- 2017AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networkscited by 29
- 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
- 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
- 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
- 2021Over-the-Air Decentralized Federated Learningcited by 18
- 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
- 2020Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?cited by 3
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