MERIDIAN: Bridging Linguistic Heterogeneity in Federated Vision-Language Learning

Federated Vision-Language Models (FedVLMs) allow for distributed clients to collaboratively fine-tune a pretrained model like CLIP while preserving privacy. Current systems (PromptFL, FedPGP, FedOTP, FedPHA, pFedMoAP) provide good personalization in single-language settings but fail to consider an important real-world axis of heterogeneity: linguistic diversity. Standard federated text-adapter aggregation mixes morphologically different language signals when clients communicate in languages other than high-resource languages like English or Chinese and low-resource languages like Bengali or Tamil, thus hurting both personalization and generalization. This introduces MERIDIAN: the first FedVLM framework for explicitly dealing with multilingual heterogeneity. MERIDIAN adds three novel components: (i) a Language-Family Text Adapter Bank (LF-TAB) with cross-lingual family-clustered FedAvg aggregation, which avoids cross-family gradient interference; (ii) a Personalized Per-Language Text Adapter, kept locally at each client to capture language-specific idiosyncrasies of the text; and (iii) a Cross-Lingual Anchor Loss (L_CLAL) that ensures consistency of the visual representations with the pivot-language Text Embedding, enforcing cross-lingual semantic consistency. After extensive experiments across 10 benchmark datasets, 8 baselines, and 10 languages from 6 language families, MERIDIAN obtains an average H-Mean of 83.73% (+7.75 average gain over the best previous approach, pFedMoAP) and gains an average of +1.96 H-Mean on low-resource language clients (Bengali, Swahili, Tamil) and a peak per-family gain of +2.31 H-Mean on the Indic-Bengali language family.