arXiv:2606.16655v1 Announce Type: new Abstract: One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data distributions. In particular, the interaction of domain shift and label shift across clients induces misaligned feature representations that cannot be corrected through iterative optimization. Existing OSFL methods rely on distillation, server-side generation or ensemble-based aggregation, but assume aligned representations or address domain and label shift separately. W
The increasing complexity and heterogeneity of data in real-world ML applications necessitates more robust and efficient distributed learning methods like One-Shot Federated Learning.
This research addresses a core challenge in federated learning regarding data heterogeneity, which, if solved, could significantly expand the applicability and efficiency of privacy-preserving machine learning.
Improved distribution alignment techniques could unlock more effective one-shot federated learning, reducing communication overhead and allowing for deployment in more communication-constrained or privacy-sensitive environments.
- · Machine Learning Researchers
- · Organizations with sensitive data
- · Edge AI developers
- · Centralized model training paradigms
More efficient and private AI model training becomes feasible for a wider range of applications.
This could accelerate the development of AI systems that learn directly from distributed, disjoint datasets without centralizing raw data.
Enhanced one-shot federated learning might contribute to the proliferation of localized AI agents that can rapidly adapt with minimal data exchange.
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Read at arXiv cs.LG