Incentivized Federated Learning: A Survey

Federated Learning (FL) is an emerging learning paradigm facilitating privacy-preserving machine learning at a large scale without the need for training data aggregation. The existing literature on FL mainly focuses on optimizing learning to enhance convergence time and accuracy. However, the need for developing incentivisation strategies to motivate clients to actively participate in training is a highly relevant area of research within FL.Considering the relevance of the problem,the need for a comprehensive review of incentive mechanism development and working in FL is really high. Initially, this survey provides a basic introduction into FL and incentive mechanisms. Secondly, the fundamental aspects of incentivisation are covered, including formally defining incentivisation, the rationale behind incentivisation, benefits of incentivising clients, implementation methods, and aspects of bias. Next, different types of incentive distribution mechanisms, auction theory, contract theory, and game theory are introduced and discussed in detail, citing methodologies and limitations. Following this, the survey presents contribution evaluation mechanisms, discussing the detailed workings of Shapley value and reputation-based systems. Motivated by the inferences generated, studies on fairness during incentivisation are also presented. Lastly, some of the major considerations in incentivized FL related to cross-silo applications, free-riders, straggler mitigation, and blockchain systems are presented, followed by a discussion on open research problems and prospective research directions. Collectively, the study provides readers with a state-of-the-art and comprehensive perspective on incentivisation in FL, hoping to be a benchmark for future researchers studying fundamental aspects and applications across various domains.