Maverick Matters: Client Contribution and Selection in Federated Learning

Federated learning (FL) enables collaborative learning between parties, called clients, without sharing the original and potentially sensitive data. To ensure fast convergence in the presence of such heterogeneous clients, it is imperative to timely select clients who can effectively contribute to learning. A realistic but overlooked case of...

Efficient Shapley Value Approximation Methods: for Cost Redistribution in Energy Communities

With the emergence of energy communities, where a number of prosumers (consumers with their own energy generation) invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value, a solution concept in cooperative game theory initially...

Shapley Values: A Comparison of Definitions and Approximation Methods

The Shapley value method is an explanatory method that describes the feature attribution of Machine Learning models. There are three different definitions of the Shapley values, namely Conditional Expectation Shapley, Marginal Expectation Shapley and Baseline Shapley. A comparison is made between the three definitions and they are applied to one...