Federated Learning Clients Clustering with Adaptation to Data Drifts

• URL: http://arxiv.org/abs/2411.01580v1
• Date: Sun, 03 Nov 2024 14:13:38 GMT
• Title: Federated Learning Clients Clustering with Adaptation to Data Drifts
• Authors: Minghao Li, Dmitrii Avdiukhin, Rana Shahout, Nikita Ivkin, Vladimir Braverman, Minlan Yu,
• Abstract summary: Federated Learning (FL) enables deep learning model training across edge devices. In this paper, we introduce Fielding, a clustered FL framework that handles data drifts promptly with low overheads. Our evaluations show that Fielding improves model final accuracy by 1.9%-5.9% and reaches target accuracies 1.16x-2.61x faster.
• Score: 27.974937897248132
• License:
• Abstract: Federated Learning (FL) enables deep learning model training across edge devices and protects user privacy by retaining raw data locally. Data heterogeneity in client distributions slows model convergence and leads to plateauing with reduced precision. Clustered FL solutions address this by grouping clients with statistically similar data and training models for each cluster. However, maintaining consistent client similarity within each group becomes challenging when data drifts occur, significantly impacting model accuracy. In this paper, we introduce Fielding, a clustered FL framework that handles data drifts promptly with low overheads. Fielding detects drifts on all clients and performs selective label distribution-based re-clustering to balance cluster optimality and model performance, remaining robust to malicious clients and varied heterogeneity degrees. Our evaluations show that Fielding improves model final accuracy by 1.9%-5.9% and reaches target accuracies 1.16x-2.61x faster.

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