Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
CoRR(2024)
摘要
Federated Learning (FL) revolutionizes collaborative machine learning among
Internet of Things (IoT) devices by enabling them to train models collectively
while preserving data privacy. FL algorithms fall into two primary categories:
synchronous and asynchronous. While synchronous FL efficiently handles
straggler devices, it can compromise convergence speed and model accuracy. In
contrast, asynchronous FL allows all devices to participate but incurs high
communication overhead and potential model staleness. To overcome these
limitations, the semi-synchronous FL framework introduces client tiering based
on computing and communication latencies. Clients in different tiers upload
their local models at distinct frequencies, striking a balance between
straggler mitigation and communication costs. Enter the DecantFed algorithm
(Dynamic client clustering, bandwidth allocation, and local training for
semi-synchronous Federated learning), a dynamic solution that optimizes client
clustering, bandwidth allocation, and local training workloads to maximize data
sample processing rates. Additionally, DecantFed adapts client learning rates
according to their tiers, addressing the model staleness problem. The
algorithm's performance shines in extensive simulations using benchmark
datasets, including MNIST and CIFAR-10, under independent and identically
distributed (IID) and non-IID scenarios. DecantFed outpaces FedAvg and FedProx
in terms of convergence speed and delivers a remarkable minimum 28
model accuracy compared to FedProx.
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