MCMFL: Monte-Carlo-Dropout-Based Multimodal Federated Learning for Giant Models in 6G Symbiotic Internet of Things

Jinhua Chen, Yuning Qiu, Qibin Zhao, Guolong Chen, Osama Alfarraj, Keping Yu

Abstract: Giant AI models, typically trained and deployed centrally in the cloud, demand significant computational resources, posing privacy risks for the Internet of Things (IoT), particularly in the era of 6G-driven connectivity. federated learning (FL) mitigates this by enabling local training and server-side aggregation, fostering 6G Symbiotic IoT while preserving privacy in 6G networks. However, data heterogeneity (DH) in multimodal settings remains a formidable challenge, degrading model performance. While prior studies attribute DH to uneven data distributions, our empirical analysis reveals that hard samples also drive DH, manifesting across both local and global models. To address this, we propose MCMFL, a multimodal FL framework leveraging Monte Carlo dropout to quantify sample uncertainty and identify hard samples. Exploiting 6G’s excellent capabilities, MCMFL optimizes the local loss function and introduces MC dropout-based aggregation, a robust aggregation algorithm, enhancing the model’s resilience to hard samples. Extensive experiments show that MCMFL demonstrates superior performance, outperforming baseline aggregation methods by up to 5.38% on CIFAR-100, leading local enhancement baselines by 3.14% on TinyImageNet-200 and achieving the highest score of 4.79 in MTBenchmark for large language model. By shifting the focus from data distribution to sample-level uncertainty, MCMFL provides a novel framework for deploying large AI models via FL in IoT scenarios, mitigating the critical challenge of DH and enhancing model robustness.

External IDs:dblp:journals/iotj/ChenQZCAY25