DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning

TL;DR

DynamicFL is a novel federated learning framework that optimizes client selection by considering network dynamics and data quality, significantly improving training efficiency and model accuracy in highly dynamic edge environments.

Contribution

We introduce DynamicFL, a client manipulation and selection strategy that balances communication delays and data quality, enhancing federated learning performance under network heterogeneity.

Findings

Achieves better model accuracy than state-of-the-art methods.

Reduces training time by up to 81.1%.

Demonstrates robustness across various real-world scenarios.

Abstract

Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a global model by exploiting the decentralized data across millions of edge devices. Compared with centralized learning, FL preserves the clients' privacy by refraining from explicitly downloading their data. However, given the geo-distributed edge devices (e.g., mobile, car, train, or subway) with highly dynamic networks in the wild, aggregating all the model updates from those participating devices will result in inevitable long-tail delays in FL. This will significantly degrade the efficiency of the training process. To resolve the high system heterogeneity in time-sensitive FL scenarios, we propose a novel FL framework, DynamicFL, by considering the communication dynamics and data quality across massive edge devices with a specially designed client manipulation strategy. \ours actively selects clients for model updating based on the network prediction from its dynamic network conditions and the quality of its training data. Additionally, our long-term greedy strategy in client selection tackles the problem of system performance degradation caused by short-term scheduling in a dynamic network. Lastly, to balance the trade-off between client performance evaluation and client manipulation granularity, we dynamically adjust the length of the observation window in the training process to optimize the long-term system efficiency. Compared with the state-of-the-art client selection scheme in FL, \ours can achieve a better model accuracy while consuming only 18.9\% -- 84.0\% of the wall-clock time. Our component-wise and sensitivity studies further demonstrate the robustness of \ours under various real-life scenarios.