Work

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-Synchronous Federated Learning

Year: 2024

Type: article

Abstract: 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, its convergence speed and model accuracy can be compromised. In contrast, asynchronous FL allows all devices to participate but incurs high communication overhead and potential model staleness. To overcome these limitations, the paper introduces a semi-synchronous FL framework that uses 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. Building on this, the paper proposes the Dynamic client clustering, bandwidth allocation, and local training for semi-synchronous Federated learning (DecantFed) algorithm to dynamically optimize client clustering, bandwidth allocation, and local training workloads in order to maximize data sample processing rates in FL. DecantFed dynamically optimizes client clustering, bandwidth allocation, and local training workloads for maximizing data processing rates in FL. It also adapts client learning rates according to their tiers, thus addressing the model staleness issue. Extensive simulations using benchmark datasets like MNIST and CIFAR-10, under both IID and non-IID scenarios, demonstrate DecantFed’s superior performance. It outperforms FedAvg and FedProx in convergence speed and delivers at least a 28% improvement in model accuracy, compared to FedProx.

Source: Electronics

DOI: 10.3390/electronics13234585

Authors: Liangkun Yu, Xiang Sun, Rana Albelaihi, Chaeeun Park, Sihua Shao

Institutions: University of New Mexico, Onaizah Colleges, Colorado School of Mines

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: National Science Foundation