FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
arXiv:2606.31742v1 Announce Type: new Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users. However, the potential of XAI beyond providing model transparency has remained largely unexplored in adjacent machine learning domains. In this paper, we show for the first time how XAI can be utilized in the context of federated learning. Specifically, while federated learning enables collaborative model training without raw data sharing,
The paper leverages recent advancements in Explainable AI (XAI) to solve a critical issue in Federated Learning (FL), addressing challenges related to data heterogeneity as FL deployments become more common.
This research provides a mechanism to improve the robustness and reliability of Federated Learning models, making them more applicable in diverse real-world scenarios where data is inherently varied and distributed.
The integration of XAI into Federated Learning moves beyond mere interpretability, offering a new methodological approach to enhance model performance and potentially expand FL's practical applications.
- · Federated Learning platforms
- · Healthcare sector (data privacy)
- · Financial services (data privacy)
- · Explainable AI researchers
- · Traditional centralized machine learning approaches
More robust and efficient federated learning implementations are developed.
Increased adoption of federated learning in sensitive data environments due to improved reliability and interpretability.
New regulatory frameworks may emerge that mandate XAI integration for privacy-preserving AI systems.
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Read at arXiv cs.LG