Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
arXiv:2505.19699v2 Announce Type: replace-cross Abstract: Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy. However, the coexistence of model and data heterogeneity gives rise to inconsistent representations and divergent optimization dynamics across clients, ultimately hindering robust global performance. To transcend these challenges, we propose Mosaic, a novel data-free knowledge distillation framework tailored for heterogeneous distributed environments. Mosaic first trains local generativ
The proliferation of distributed data and computational resources necessitates new approaches to AI training that can maintain performance while preserving privacy and navigating heterogeneity.
This research addresses fundamental challenges in federated learning, potentially enabling more robust and efficient AI model development in diverse and privacy-sensitive environments.
A framework like Mosaic could allow for more effective knowledge transfer and model aggregation in heterogeneous distributed systems without direct access to sensitive raw data.
- · AI companies working with distributed data
- · Healthcare sector
- · Financial institutions
- · Privacy-focused AI applications
- · Centralized model training paradigms
- · Organizations without privacy-preserving AI strategies
Improved performance and broader applicability of federated learning in real-world, heterogeneous settings.
Accelerated adoption of privacy-preserving AI across sensitive industries due to enhanced model robustness.
Reduced data governance friction for cross-organizational AI collaborations, potentially fostering new distributed AI ecosystems.
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Read at arXiv cs.AI