FedOptima: Optimizing Resource Utilization in Federated Learning

Abstract

Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low resource utilization on servers and devices, limiting their practical use in the real world. This inefficiency primarily arises from two types of idle time: (i) task dependency between the server and devices, and (ii) stragglers among heterogeneous devices. This paper introduces FedOptima, a resource-optimized FL system designed to simultaneously minimize both types of idle time; existing systems do not eliminate or reduce both at the same time. FedOptima offloads the training of certain layers of a neural network from a device to a server using three innovations. First, devices operate independently of each other using asynchronous aggregation to eliminate straggler effects, and independently of the server by utilizing auxiliary networks to minimize idle time caused by task dependency. Second, the server performs centralized training using a task scheduler that ensures balanced contributions from all devices, improving model accuracy. Third, an efficient memory management mechanism on the server increases the scalability of the number of participating devices. Extensive experiments are conducted on multiple lab-based testbeds, evaluated on image classification and sentiment analysis tasks with CNNs and Transformers. Compared to four state-of-the-art offloading-based and asynchronous FL baselines, FedOptima (i) achieves higher or comparable accuracy, (ii) accelerates training by 1.9x to 21.8x, (iii) reduces server and device idle time by up to 93.9% and 81.8%, respectively, and (iv) increases throughput by 1.1x to 2.0x.

Accepted for publication in Future Generation Computer Systems

References in corpus (12)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Learning with Differential Privacy
• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
• Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
• Resource-Efficient Federated Learning
• Oort: Efficient Federated Learning via Guided Participant Selection
• Decoupled Greedy Learning of CNNs
• Robust Split Federated Learning for U-shaped Medical Image Networks
• Communication and Storage Efficient Federated Split Learning
• FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting