FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments

2025-03-25

AI Summary

In distributed dynamic settings, multimodal federated learning suffers from compromised trustworthiness and interpretability due to data heterogeneity, modality imbalance, and out-of-distribution generalization. To address this, we propose a triple-cooperative framework: (1) cross-modal consistency verification to enforce semantic alignment across modalities; (2) client-level gradient attribution explanation (Modality-Aware Grad-CAM) for fine-grained interpretability; and (3) a dynamic trust calibration mechanism integrating global trust scoring and adaptive aggregation to quantify model reliability. Our method synergistically combines federated learning, multimodal alignment modeling, consistency regularization, and trust-aware weighted aggregation. Evaluated on multiple vision-language federated benchmarks, it achieves an average accuracy gain of 3.2%, improves explanation fidelity by 18.7%, reduces vulnerability to adversarial perturbations and spurious correlations by 41%, and enables real-time, adaptive trust assessment.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As artificial intelligence systems increasingly operate in Real-world environments, the integration of multi-modal data sources such as vision, language, and audio presents both unprecedented opportunities and critical challenges for achieving trustworthy intelligence. In this paper, we propose a novel framework that unifies federated learning with explainable multi-modal reasoning to ensure trustworthiness in decentralized, dynamic settings. Our approach, called FedMM-X (Federated Multi-Modal Explainable Intelligence), leverages cross-modal consistency checks, client-level interpretability mechanisms, and dynamic trust calibration to address challenges posed by data heterogeneity, modality imbalance, and out-of-distribution generalization. Through rigorous evaluation across federated multi-modal benchmarks involving vision-language tasks, we demonstrate improved performance in both accuracy and interpretability while reducing vulnerabilities to adversarial and spurious correlations. Further, we introduce a novel trust score aggregation method to quantify global model reliability under dynamic client participation. Our findings pave the way toward developing robust, interpretable, and socially responsible AI systems in Real-world environments.

Problem

• Ensuring trustworthiness in federated multi-modal learning
• Addressing data heterogeneity and modality imbalance
• Improving interpretability and reliability in dynamic environments

Innovation

• Federated learning with explainable multi-modal reasoning
• Cross-modal consistency checks for trustworthiness
• Dynamic trust calibration for reliability