Explainable AI for Federated Learning – Attribution of Contributions

Abstract

Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent lack of transparency in FL raises concerns about trust, fairness, and model reliability. This paper explores the critical need for explainable AI (XAI) techniques within the FL paradigm, specifically focusing on attributing contributions from individual participants to the global model. We propose a novel framework for tracing the influence of local model updates, quantifying the impact of each participant's contribution on the global model's parameters. Our methodology leverages gradient analysis and differential privacy considerations to provide a granular understanding of the learning process. The core claim is that understanding these contributions is essential for fostering trust and ensuring fairness in FL deployments. The developed attribution methods provide insights into the learning dynamics, allowing for the identification of potential biases or anomalous behavior. This work aims to establish a foundation for more robust and accountable FL systems.