Window-based Model Averaging Improves Generalization in Heterogeneous
Federated Learning
- URL: http://arxiv.org/abs/2310.01366v1
- Date: Mon, 2 Oct 2023 17:30:14 GMT
- Title: Window-based Model Averaging Improves Generalization in Heterogeneous
Federated Learning
- Authors: Debora Caldarola, Barbara Caputo, Marco Ciccone
- Abstract summary: Federated Learning (FL) aims to learn a global model from distributed users while protecting their privacy.
We propose WIMA (Window-based Model Averaging), which aggregates global models from different rounds using a window-based approach.
Our experiments demonstrate the robustness of WIMA against distribution shifts and bad client sampling, resulting in smoother and more stable learning trends.
- Score: 29.140054600391917
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated Learning (FL) aims to learn a global model from distributed users
while protecting their privacy. However, when data are distributed
heterogeneously the learning process becomes noisy, unstable, and biased
towards the last seen clients' data, slowing down convergence. To address these
issues and improve the robustness and generalization capabilities of the global
model, we propose WIMA (Window-based Model Averaging). WIMA aggregates global
models from different rounds using a window-based approach, effectively
capturing knowledge from multiple users and reducing the bias from the last
ones. By adopting a windowed view on the rounds, WIMA can be applied from the
initial stages of training. Importantly, our method introduces no additional
communication or client-side computation overhead. Our experiments demonstrate
the robustness of WIMA against distribution shifts and bad client sampling,
resulting in smoother and more stable learning trends. Additionally, WIMA can
be easily integrated with state-of-the-art algorithms. We extensively evaluate
our approach on standard FL benchmarks, demonstrating its effectiveness.
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