Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation

• URL: http://arxiv.org/abs/2603.20873v1
• Date: Sat, 21 Mar 2026 16:33:36 GMT
• Title: Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation
• Abstract summary: Federated learning (FL) is a collaborative learning framework that enables model training across multiple agents with private local datasets.<n>Motivated by the need for FL-aware incentive that successfully retain participating agents, we propose a method in which clients transmit both local model parameters and their updated training training.<n>Experiments demonstrate that agents achieve competitive global model performance while converging to stable data participation strategies.
• Score: 2.931240348160871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is a communication-efficient collaborative learning framework that enables model training across multiple agents with private local datasets. While the benefits of FL in improving global model performance are well established, individual agents may behave strategically, balancing the learning payoff against the cost of contributing their local data. Motivated by the need for FL frameworks that successfully retain participating agents, we propose an incentive-aware federated averaging method in which, at each communication round, clients transmit both their local model parameters and their updated training dataset sizes to the server. The dataset sizes are dynamically adjusted via a Nash equilibrium (NE)-seeking update rule that captures strategic data participation. We analyze the proposed method under convex and nonconvex global objective settings and establish performance guarantees for the resulting incentive-aware FL algorithm. Numerical experiments on the MNIST and CIFAR-10 datasets demonstrate that agents achieve competitive global model performance while converging to stable data participation strategies.

Related papers

• Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data [4.9856574965240545]
  Federated Learning (FL) enables distributed training of machine learning models while preserving data privacy.<n>We propose pFedMARL, a novel approach leveraging Multi-Agent Reinforcement Learning (MARL) with Twin Delayed Deep Deterministic Policy Gradient (TD3)<n>Our method employs a server-side agent adjusting client contributions to optimize global model robustness and client-side agents balancing global and local updates to personalize models effectively without pre-training.
  arXiv  Detail & Related papers  (2026-08-26T13:46:43Z)
• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning [0.0]
  FedDPC is a novel FL method designed to improve FL training and global model performance.<n>We show that FedDPC outperforms state-of-the-art FL algorithms by achieving faster reduction in training loss and improved test accuracy across communication rounds.
  arXiv  Detail & Related papers  (2025-12-23T12:57:27Z)
• CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks [51.43780477302533]
  Contribution-Oriented PFL (CO-PFL) is a novel algorithm that dynamically estimates each client's contribution for global aggregation.<n>CO-PFL consistently surpasses state-of-the-art methods in robustness in personalization accuracy, robustness, scalability and convergence stability.
  arXiv  Detail & Related papers  (2025-10-23T05:10:06Z)
• Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning [0.9176056742068811]
  Federated learning (FL) enables distributed training with private client data.<n>Current ensemble-based FL methods fall short in capturing diversity of model predictions.<n>We propose textbfSHEFL, a global ensemble-based FL framework suited for clients with diverse computational capacities.
  arXiv  Detail & Related papers  (2025-08-12T01:40:46Z)
• SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning [15.256986486372407]
  Spiking federated learning allows resource-constrained devices to train collaboratively at low power consumption without exchanging local data. Existing spiking federated learning methods employ a random selection approach for client aggregation, assuming unbiased client participation. We propose a credit assignment-based active client selection strategy, the SFedCA, to judiciously aggregate clients that contribute to the global sample distribution balance.
  arXiv  Detail & Related papers  (2024-06-18T01:56:22Z)
• FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems [14.378330471182586]
  Federated learning (FL) promises to enable machine learning across healthcare sites whilst preserving data privacy.<n>We introduce FedMAP, a personalised FL framework that addresses statistical heterogeneity through local Maximum a Posteriori (MAP) estimation.<n>We demonstrate improved performance compared to local training, FedAvg, and several PFL methods across three large-scale clinical datasets.
  arXiv  Detail & Related papers  (2024-05-29T11:28:06Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Towards Robust Federated Learning via Logits Calibration on Non-IID Data [49.286558007937856]
  Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. Recent studies have shown that FL is vulnerable to adversarial examples, leading to a significant drop in its performance. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks.
  arXiv  Detail & Related papers  (2024-03-05T09:18:29Z)
• Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems [2.8790600498444032]
  We propose a dynamic global model aggregation method within Asynchronous Federated Learning (AFL) deployments. Our method scores and adjusts the weighting of client model updates based on their upload frequency to accommodate differences in device capabilities.
  arXiv  Detail & Related papers  (2024-01-24T10:51:15Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning [9.975023463908496]
  Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. We propose a novel regularization technique based on adaptive self-distillation (ASD) for training models on the client side. Our regularization scheme adaptively adjusts to the client's training data based on the global model entropy and the client's label distribution.
  arXiv  Detail & Related papers  (2023-05-31T07:00:42Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.