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GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniques

arXiv 2212.04103 ↗First posted 8 Dec 2022cs.LG cs.AI cs.DCcited by 0

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Abstract (arXiv)

GTFLAT, as a game theory-based add-on, addresses an important research question: How can a federated learning algorithm achieve better performance and training efficiency by setting more effective adaptive weights for averaging in the model aggregation phase? The main objectives for the ideal method of answering the question are: (1) empowering federated learning algorithms to reach better performance in fewer communication rounds, notably in the face of heterogeneous scenarios, and last but not least, (2) being easy to use alongside the state-of-the-art federated learning algorithms as a new module. To this end, GTFLAT models the averaging task as a strategic game among active users. Then it proposes a systematic solution based on the population game and evolutionary dynamics to find the equilibrium. In contrast with existing approaches that impose the weights on the participants, GTFLAT concludes a self-enforcement agreement among clients in a way that none of them is motivated to deviate from it individually. The results reveal that, on average, using GTFLAT increases the top-1 test accuracy by 1.38%, while it needs 21.06% fewer communication rounds to reach the accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 8 Dec 2022.

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20 of 38 references.

References · 20

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8 more
  1. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  2. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  3. 2020Can Federated Learning Save The Planet?cited by 52
  4. 2020Inverse Distance Aggregation for Federated Learning with Non-IID Datacited by 44
  5. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  6. 2021Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learningcited by 23
  7. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  8. 2022Optimising Communication Overhead in Federated Learning Using NSGA-IIcited by 7

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