FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
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 col
Record details
Published: 30 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Transparency
Retrieved: 14 July 2026
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ethics.ai (30 June 2026), “FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning,” evidence record 389, https://ethics.ai/record/389 (originally published by arXiv).
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