Incentive Mechanisms for Federated Learning

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10 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Pay to Learn, Share to Earn: Incentivized Federated Multi-Player Bandits
Oct 5, 2026Pavamana K J, Chandramani SinghMulti-Armed Bandits Incentive Mechanisms for Federated Learning
Indian Institute of Science, Bengaluru, India
Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design
Sep 13, 2026Amin Meghrazi, Srinivasan Parthasarathy, Andrew PerraultFederated Learning Incentive Mechanisms for Federated Learning
The Ohio State University
Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach
Jul 29, 2026Cengis HasanCooperative Game Theory Coalition Formation
Cognifinity, Luxembourg
Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
May 6, 2026Leon Witt, Togrul Abbasli, Kentaroh Toyoda +2Incentive Mechanisms for Federated Learning Federated Learning
SIMIS Shanghai Shanghai, China · Tsinghua University Beijing, China · Keio University Yokohama, Japan +1
Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning
Apr 16, 2026Thanh Linh Nguyen, Nguyen Van Huynh, Quoc-Viet PhamSynthetic Data Generation Potential Games
School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, D02 PN40, Ireland · School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, UK
FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
Apr 12, 2026Tao Liu, Xuehe WangIncentive Mechanisms for Federated Learning Federated Learning
School of Artificial Intelligence, Sun Yat-sen University, Zhuhai 519082, China
Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation
Mar 21, 2026Fateme Maleki, Krishnan Raghavan, Farzad YousefianIncentive Mechanisms for Federated Learning Federated Learning
Department of Industrial and Systems Engineering, Rutgers University, USA. · Argonne National Laboratory, Lemont, IL, USA.
SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)
Feb 26, 2026Shuang Liang, Yang Hua, Linshan Jiang +4Incentive Mechanisms for Federated Learning Blockchain-Based Federated Learning
Shanghai Jiao Tong University · Queen's University Belfast · Queen’s University Belfast +1
JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Feb 25, 2026Ruichen Xu, Ying-Jun Angela Zhang, Jianwei HuangIncentive Mechanisms for Federated Learning Client Selection in Federated Learning
Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong · School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network, and CSIJRI Joint Research Centre on Smart Energy Storage, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P.R. China
Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners
May 17, 2025Drashthi Doshi, Avishek Ghosh, Swaprava NathIncentive Mechanisms for Federated Learning Federated Learning
IIT Bombay

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Pay to Learn, Share to Earn: Incentivized Federated Multi-Player Bandits

Pavamana K J, Chandramani Singh

Indian Institute of Science, Bengaluru, India

Multi-Armed Bandits Incentive Mechanisms for Federated Learning UCB Algorithms Federated Learning

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Federated multi-player multi-armed bandit problems model collaborative sequential decision-making where multiple players interact with a common bandit environment and share information through a central server to accelerate learning. Existing federated bandit frameworks typically assume that all players willingly share their local observations with the server. However, this assumption is often unrealistic in practical settings where players are self-interested and may not participate in collaboration without explicit incentives. To address this challenge, we propose an incentive-aware federated bandit framework in which players receive rewards for sharing information with the server and incur costs when buying information from the server. We develop a UCB-based algorithm, termed Buying-UCB, that balances individual exploration and collaborative learning by incorporating both sharing incentives and information acquisition costs into the learning process. We theoretically analyze the proposed algorithm and derive upper bounds on the group regret and buying cost. Our analysis further characterizes the trade-off between fully collaborative federated learning and completely independent learning. Extensive numerical experiments validate the theoretical findings and demonstrate the effectiveness of the proposed framework under different collaboration and pricing regimes.