Asynchronous federated learning over TDMA channels is claimed to converge at rate O(G^2/√K) with device group count G, but the proof relies on a false inequality, and an intentional delay reduces staleness in experiments.
Optimal Batch Allocation for Wireless Federated Learning
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abstract
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.
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Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel
Asynchronous federated learning over TDMA channels is claimed to converge at rate O(G^2/√K) with device group count G, but the proof relies on a false inequality, and an intentional delay reduces staleness in experiments.