ShuffleFL

ShuffleFL: Addressing Heterogeneity in Multi-Device Federated Learning

Zhu, R. (TU Delft Embedded Systems)
Yang, M. (TU Delft Embedded Systems)
Wang, Q. (TU Delft Embedded Systems)

2024

Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative deep learning model training across distributed data silos. Despite its importance, FL faces challenges such as high latency and less effective global models. In this paper, we propose ShuffleFL, an innovative framework stemming from the hierarchical FL, which introduces a user layer between the FL devices and the FL server. ShuffleFL naturally groups devices based on their affiliations, e.g., belonging to the same user, to ease the strict privacy restriction-"data at the FL devices cannot be shared with others", thereby enabling the exchange of local samples among them. The user layer assumes a multi-faceted role, not just aggregating local updates but also coordinating data shuffling within affiliated devices. We formulate this data shuffling as an optimization problem, detailing our objectives to align local data closely with device computing capabilities and to ensure a more balanced data distribution at the intra-user devices. Through extensive experiments using realistic device profiles and five non-IID datasets, we demonstrate that ShuffleFL can improve inference accuracy by 2.81% to 7.85% and speed up the convergence by 4.11x to 36.56x when reaching the target accuracy.

Data Heterogeneity
Data Shuffling
Federated Learning
IoT
System Heterogeneity

http://resolver.tudelft.nl/uuid:c87ca2d1-1794-4680-a050-a08da2a990ff

https://doi.org/10.1145/3659621

2474-9567

ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8 (2)

Institutional Repository

journal article

© 2024 R. Zhu, M. Yang, Q. Wang