Proximal visual prompt tuning rectifies non-IID client features so analytic least-squares aggregation yields strong one-shot federated classifiers with zero server training cost.
Nav- igating heterogeneity and privacy in one-shot feder- ated learning with diffusion models
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However, FL faces challenges in terms of communication cost and data heterogeneity. One-shot federated learning has emerged as a solution by reducing communication rounds, improving efficiency, and providing better security against eavesdropping attacks. Nevertheless, data heterogeneity remains a significant challenge, impacting performance. This work explores the effectiveness of diffusion models in one-shot FL, demonstrating their applicability in addressing data heterogeneity and improving FL performance. Additionally, we investigate the utility of our diffusion model approach, FedDiff, compared to other one-shot FL methods under differential privacy (DP). Furthermore, to improve generated sample quality under DP settings, we propose a pragmatic Fourier Magnitude Filtering (FMF) method, enhancing the effectiveness of generated data for global model training.
representative citing papers
citing papers explorer
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FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
Proximal visual prompt tuning rectifies non-IID client features so analytic least-squares aggregation yields strong one-shot federated classifiers with zero server training cost.
- CollaFuse: Collaborative Diffusion Models