Explore the cutting-edge research paper titled
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
by Sajjad Ghiasvand and his colleagues, and discover how this innovative framework revolutionizes the adaptation of Vision-Language Models (VLMs).
Abstract:
Vision-Language Models (VLMs) like CLIP have demonstrated remarkable generalization in zero- and few-shot settings, but adapting them efficiently to decentralized, heterogeneous data remains a challenge. While prompt tuning has emerged as a popular parameter-efficient approach in personalized federated learning, existing methods often sacrifice generalization in favor of personalization, struggling particularly on unseen classes or domains. In this work, we propose pFedMMA, the first personalized federated learning framework that leverages multi-modal adapters for vision-language tasks. Each adapter contains modality-specific up- and down-projection layers alongside a globally shared projection that aligns cross-modal features. Our optimization strategy allows clients to locally adapt to personalized data distributions while collaboratively training the shared projection to improve global generalization. This design is also communication-efficient, as only the shared component is exchanged during communication rounds. Through extensive experiments across eleven datasets, including domain- and label-shift scenarios, we show that pFedMMA achieves state-of-the-art trade-offs between personalization and generalization, outperforming recent federated prompt tuning methods.
Submission History
From: Sajjad Ghiasvand [view email]
[v1] Mon, 7 Jul 2025 18:26:34 UTC (351 KB)
[v2] Fri, 26 Sep 2025 10:36:44 UTC (439 KB)
[v3] Fri, 27 Feb 2026 00:14:04 UTC (436 KB)
What is pFedMMA?
The pFedMMA framework introduces a transformative approach to personalized federated learning. As organizations increasingly rely on diverse datasets sourced from numerous clients, the ability to adapt machine learning models effectively becomes critical. Traditional Vision-Language Models, such as CLIP, offer impressive performance but fall short when faced with the challenge of personalized adaptations that maintain robust generalization. The backdrop of decentralized, heterogeneous data complicates this task, making pFedMMA a significant advancement in this space.
The Problem with Current Approaches
Existing methods, primarily based on prompt tuning, often prioritize individual model personalization over global model generalization. This compromise can lead to reduced performance when confronted with unseen classes or domains. As a result, researchers and professionals have been exploring alternatives that enable a more effective balance between these two seemingly opposite objectives. The introduction of pFedMMA seeks to redefine this balance by ensuring that while clients tame their local adaptations, they simultaneously contribute to a shared model that benefits from collective intelligence.
Key Features of pFedMMA
At its core, pFedMMA utilizes multi-modal adapters tailored specifically for vision-language tasks. Each adapter comprises unique up- and down-projection layers for various modalities, complemented by a globally shared projection layer. This structure empowers the model to align cross-modal features, enhancing relational learning across varying data types.
Moreover, the optimization strategy adopted within pFedMMA is designed with communication efficiency in mind. In typical federated learning environments, significant bandwidth is required for model updates. However, pFedMMA minimizes this overhead by transmitting only the shared component during communication rounds, making it more suitable for real-world applications where connectivity may be unreliable or bandwidth-limited.
Experimentation and Results
The effectiveness of pFedMMA has been demonstrated through extensive experimentation across eleven datasets, addressing both domain- and label-shift scenarios. These rigorous tests illustrate that pFedMMA not only achieves an exceptional balance between personalization and generalization but also surpasses contemporary federated prompt tuning approaches. The consistent performance of pFedMMA across diverse domains suggests a promising future for federated learning in applications that require adaptable yet robust models.
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Conclusion and Implications
While this article does not wrap up with a conclusion, the implications of pFedMMA’s advancements in personalized federated learning are far-reaching. As industries strive to harness the power of machine learning while maintaining privacy and efficiency across decentralized datasets, frameworks like pFedMMA pave the way for the next generation of adaptive, intelligent systems. This research opens new avenues for exploration and application in various fields, reinforcing the essential role of personalized learning in the age of data-driven decision-making.
For those interested in delving deeper, a PDF of the full paper is available here.
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