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Title: A Dynamic Reweighting Strategy For Fair Federated Learning
Federated learning is an emerging machine learning framework where models are trained using heterogeneous datasets collected by a large number of edge clients. Standard methods to aggregate local training models weigh each model by a fraction of data size at that client. However, such approaches result in unfairness to clients with small and unique datasets, leading to inferior accuracy of the global model at these clients. In this work, we propose a novel optimization framework called DRFL that dynamically adjusts the weight assigned to each client, and we combine it with a biased client selection strategy, both of which encourage fairness in federated training. We validate the effectiveness of our proposed method on a suite of both synthetic and real federated datasets, revealing the proposed method outperforms existing baselines in terms of resulting fairness.  more » « less
Award ID(s):
2045694
NSF-PAR ID:
10389399
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Page Range / eLocation ID:
8772 to 8776
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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