arXiv:2606.29322v1 Announce Type: new Abstract: Collaborative learning is sustainable only when it benefits each participant. Standard federated learning optimizes a global average objective, which can under perform for clients whose data distributions differ substantially from the population. We study selfish personalization: how a designated target client can use peer gradients to minimize its own risk while avoiding negative transfer. We propose SP-CACW, a convergence-aware client-weighting framework that selects aggregation weights by minimizing an upper bound on the target client's conver
The increasing scale and distribution of AI models necessitate more sophisticated and equitable federated learning approaches to handle diverse client data and self-interest.
This research addresses a critical challenge in collaborative AI by enabling individual participants to benefit from shared learning, which is essential for broad adoption and trust in federated systems, especially for personalized applications.
The development of convergence-aware client weighting allows for personalized AI models that minimize negative transfer and prioritize individual utility within a collaborative framework, unlike traditional methods focused solely on global optimization.
- · AI developers
- · Data privacy-focused businesses
- · Decentralized AI platforms
- · Healthcare sector
- · One-size-fits-all AI model providers
- · Centralized data aggregators
Individual clients can achieve better model performance tailored to their specific data without sacrificing privacy or being disadvantaged by collective learning.
This could accelerate the adoption of federated learning in sensitive domains like healthcare or personal finance, where data sovereignty and personalization are paramount.
The increased confidence in decentralized, personalized AI could lead to new economic models for data sharing and AI service provision, reducing reliance on massive centralized datasets.
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Read at arXiv cs.LG