arXiv:2606.06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers. While clustering in centralized FL has enabled scalability and resource savings, its value and development in fully decentralized environments have yet to be explored. Optimizing cluster formation in such environments is challenging, especially due to the complex coupling between network graph structures, local data heterogeneity, and different local ML model optimizers.
The increasing complexity and scale of AI models necessitate more efficient and secure training methodologies that move beyond traditional centralized approaches, prompting research into decentralized solutions.
Decentralized federated learning, particularly with heterogeneous optimizers, could unlock new efficiencies and privacy-preserving capabilities for AI development, impacting data governance and computational resource allocation.
This research explores fundamental architectural changes in how federated learning clusters are formed and managed, moving towards more autonomous and adaptable systems.
- · AI developers
- · Organizations with sensitive data
- · Edge computing providers
- · Centralized cloud AI providers (potentially, over time)
- · Legacy AI training infrastructure
Improved scalability and privacy in machine learning training, especially for diverse datasets.
Reduced reliance on single points of failure and increased resilience of AI systems.
Enhanced collaboration among distinct entities on AI projects without direct data sharing, accelerating innovation in regulated sectors.
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Read at arXiv cs.LG