Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning

In this paper, we introduce CTFL, a fair, robust, and interpretable framework designed to estimate clients' contributions to federated learning, aiming to incentivize high-quality data providers to participate in the federation. Firstly, CTFL can precisely allocate contribution credits in a single pass of model training and inference, ensuring computational efficiency. This is accomplished by tracking the test performance gain brought by each participant through exploiting classification rules. Secondly, CTFL adheres to essential theoretical properties of an ideal contribution estimation algorithm, including symmetry, zero-element, and additivity, ensuring fair and rational estimations. Thirdly, CTFL demonstrates resilience against strategic and malicious behaviors due to carefully crafted micro and macro contribution estimation schemes. Fourthly, CTFL offers insights into participants' roles within the federation by interpreting their contribution scores through respective high-frequently activated rules. Finally, CTFL integrates logical neural networks and model binarization techniques to ensure effectiveness and efficiency while preserving data privacy. Extensive experiments validate that CTFL accurately estimates contributions, significantly reducing computation time by 2-3 orders of magnitude compared to state-of-the-art methods while maintaining robustness.