TAP: Two-Stage Adaptive Personalization of Multi-Task and Multi-Modal Foundation Models in Federated Learning

Researchers introduce TAP (Two-Stage Adaptive Personalization), a novel federated learning framework that enables personalized fine-tuning of foundation models across clients with heterogeneous tasks and modalities. The method uses mismatched architectures to prevent cross-task interference and post-FL distillation to recover shared knowledge, advancing practical deployment of AI systems in distributed environments.

TAP addresses a critical gap in federated learning research where foundation models must serve clients with fundamentally different computational needs and data modalities. Traditional federated learning assumes homogeneous client environments, but real-world scenarios require personalization across natural language, vision, and multimodal tasks simultaneously. The two-stage approach elegantly solves this by first isolating personalized parameters to prevent negative transfer between incompatible tasks, then reintroducing generalizable knowledge once the global model stabilizes.

This work builds on growing recognition that one-size-fits-all models harm performance in heterogeneous settings. Prior research explored personalization for data heterogeneity but largely ignored task and modality diversity. TAP's innovation lies in leveraging architectural mismatch as a feature rather than a bug—using different client and server models to enforce separation of concerns. The convergence analysis provides theoretical grounding, establishing how modality-task pair combinations affect fine-tuning dynamics.

For the AI industry, TAP has significant implications for enterprise deployment. Companies like those building healthcare or financial AI systems need models that personalize to local tasks while maintaining security and data privacy guarantees federated learning provides. The public code availability accelerates adoption and validation. This addresses scaling challenges where deploying separate specialized models becomes prohibitively expensive.

Looking ahead, practitioners should monitor whether this framework extends to extremely resource-constrained edge devices and how it performs with emerging large language models. The convergence analysis framework could inspire similar theoretical work for other federated learning scenarios.

• →TAP enables foundation models to personalize across heterogeneous tasks and modalities using two-stage adaptive training without compromising shared knowledge
• →Mismatched client-server architectures prevent negative transfer between incompatible modality-task pairs during federated learning
• →Post-FL distillation recovers generalizable structure after personalization stabilizes, balancing specialization and generalization
• →First convergence analysis for federated foundation models quantifies impact of modality-task heterogeneity on fine-tuning performance
• →Open-source implementation enables rapid adoption in privacy-preserving AI applications across enterprise and edge computing scenarios