A Robust Federated Learning Framework for Undependable Devices at Scale

TL;DR

This paper introduces FLUDE, a federated learning framework that effectively manages undependable devices by assessing device reliability, maintaining model caches, and using staleness-aware strategies to improve training efficiency and model performance.

Contribution

FLUDE is a novel federated learning approach that adaptively selects dependable devices and reduces resource wastage in unreliable environments.

Findings

Improves model accuracy in undependable device scenarios

Reduces resource wastage during training

Effective on real-world smartphone and edge device platforms

Abstract

In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks always assume a dependable environment and exclude undependable devices from training, leading to poor model performance and resource wastage. In this paper, we propose FLUDE to effectively deal with undependable environments. First, FLUDE assesses the dependability of devices based on the probability distribution of their historical behaviors (e.g., the likelihood of successfully completing training). Based on this assessment, FLUDE adaptively selects devices with high dependability for training. To mitigate resource wastage during the training phase, FLUDE maintains a model cache on each device, aiming to preserve the latest training state for later use in case local training on an undependable device is interrupted. Moreover, FLUDE proposes a staleness-aware strategy to judiciously distribute the global model to a subset of devices, thus significantly reducing resource wastage while maintaining model performance. We have implemented FLUDE on two physical platforms with 120 smartphones and NVIDIA Jetson devices. Extensive experimental results demonstrate that FLUDE can effectively improve model performance and resource efficiency of FL training in undependable environments.