Multimodal Federated Learning with Modality-Importance-Aware Compression

Multimodal Federated Learning (MFL) has drawn significant attention as a decentralized training paradigm across multimodal edge devices. However, its communication cost, which grows linearly with the number of modalities, has emerged as a distinct scalability challenge. While prior approaches mitigate this through selective transmission of modality updates, they may sacrifice complementary information essential for multimodal learning. In this paper, we introduce a modality-importance-aware compression framework for MFL, termed Fed-MIAC, which retains multimodal benefits while improving communication efficiency. Specifically, Fed-MIAC differentiates compression rates across modality-specific model updates by jointly accounting for their relative importance and compression-induced distortion. Extensive simulations on diverse MFL benchmarks demonstrate that Fed-MIAC better preserves multimodal performance than importance-agnostic compression and modality selection baselines under constrained communication budgets, also validated by wall-clock overhead evaluations on embedded edge platforms. We further provide a convergence analysis that characterizes how modality-importance-aware compression affects the convergence rate.