Oct 5, 2021 · FIFL rewards workers fairly to attract reliable and efficient ones while punishing and eliminating the malicious ones based on a dynamic real- ...
How to ensure fairness and reliability of the incentive mechanism under attacks and deceptions? 50th International Conference on Parallel Processing (ICPP).
In this paper, we propose FIFL, an incentive mechanism for federated learning in order to share profit with workers according to their behaviours and utilities.
FIFL rewards workers fairly to attract reliable and efficient ones while punishing and eliminating the malicious ones based on a dynamic real-time worker ...
In this paper, we propose an FL incentive scheme based on the reverse auction and trust reputation to select reliable clients and fairly reward clients that ...
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Abstract. Federated Learning (FL) is an innovative framework that enables work- ers to collaboratively train a global shared model in a decentralized manner ...
Sep 12, 2024 · An incentive mechanism is urgently required in order to encourage high-quality workers to participate in FL and to punish the attackers. In this ...
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Highlights · A fair incentive mechanism for federated learning is proposed. · The mechanism relies on accurately measuring workers' contributions and reputations.
FIFL: A Fair Incentive Mechanism for Federated Learning. In Proceedings of the International Conference on Parallel Processing. Gollapudi et al. (2017)
Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data ...