Resource-Efficient Federated Learning

Abstract

Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this work, we systematically address the question of resource efficiency in FL, showing the benefits of intelligent participant selection, and incorporation of updates from straggling participants. We demonstrate how these factors enable resource efficiency while also improving trained model quality.

arXiv commentAccepted to appear in ACM EuroSys 2023

References in corpus (6)

• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
• Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
• Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
• SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
• FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
• FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Cited by in corpus (8)

• FedZero: Leveraging Renewable Excess Energy in Federated Learning
• PowerTrain: Fast, Generalizable Time and Power Prediction Models to Optimize DNN Training on Accelerated Edges
• Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing
• Boosting Asynchronous Decentralized Learning with Model Fragmentation
• Accelerating MoE Model Inference with Expert Sharding
• Practical Federated Learning without a Server
• Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
• FedOptima: Optimizing Resource Utilization in Federated Learning