Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models

Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infeasible. Federated Learning mitigates this issue by enabling decentralized training, but practical deployments face challenges due to client heterogeneity in computational resources, application requirements, and model architectures. Under extreme model and data heterogeneity, replacing parameter aggregation with prefer

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ethics.ai (5 May 2026), “Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models,” evidence record 4981, https://ethics.ai/record/4981 (originally published by arXiv).

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