CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy

• URL: http://arxiv.org/abs/2509.23190v1
• Date: Sat, 27 Sep 2025 08:45:40 GMT
• Title: CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy
• Authors: Zhanhong Xie, Meifan Zhang, Lihua Yin,
• Abstract summary: CoSIFL is a framework that integrates proactive alarming for robust security and local differential privacy.<n>A Tullock contest-inspired incentive module rewards honest clients for both data contributions and reliable alarm triggers.<n>We prove that the server-client game admits a unique equilibrium, and analyze how clients' multi-dimensional attributes - such as non-IID degrees and privacy budgets - jointly affect system efficiency.
• Score: 1.1266158555540042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data locality. However, it still faces challenges from malicious or compromised clients, as well as difficulties in incentivizing participants to contribute high-quality data under strict privacy requirements. Motivated by these considerations, we propose CoSIFL, a novel framework that integrates proactive alarming for robust security and local differential privacy (LDP) for inference attacks, together with a Stackelberg-based incentive scheme to encourage client participation and data sharing. Specifically, CoSIFL uses an active alarming mechanism and robust aggregation to defend against Byzantine and inference attacks, while a Tullock contest-inspired incentive module rewards honest clients for both data contributions and reliable alarm triggers. We formulate the interplay between the server and clients as a two-stage game: in the first stage, the server determines total rewards, selects participants, and fixes global iteration settings, whereas in the second stage, each client decides its mini-batch size, privacy noise scale, and alerting strategy. We prove that the server-client game admits a unique equilibrium, and analyze how clients' multi-dimensional attributes - such as non-IID degrees and privacy budgets - jointly affect system efficiency. Experimental results on standard benchmarks demonstrate that CoSIFL outperforms state-of-the-art solutions in improving model robustness and reducing total server costs, highlighting the effectiveness of our integrated design.

Related papers

• JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning [33.2985262258717]
  Existing incentive mechanisms rely on unbiased client selection, forcing servers to compensate even the most privacy-sensitive clients.<n>We introduce JSAM, a Bayesian-optimal framework that simultaneously optimize client selection probabilities and privacy compensation.<n>We prove that servers should preferentially select privacy-tolerant clients while excluding high-sensitivity participants, and uncover the counter-intuitive insight that clients with minimal privacy sensitivity may incur the highest cumulative costs due to frequent participation.
  arXiv  Detail & Related papers  (2026-02-25T12:22:48Z)
• 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)
• Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning [50.56947843548702]
  We propose a decentralized dynamic cooperation framework for Federated continual learning.<n>Clients establish dynamic cooperative learning coalitions to balance the acquisition of new knowledge and the retention of prior learning.<n>We also propose a merge-blocking algorithm and a dynamic cooperative evolution algorithm to achieve cooperative and dynamic equilibrium.
  arXiv  Detail & Related papers  (2025-09-28T06:53:23Z)
• Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy [8.683908900328237]
  We propose GDPFed, which partitions clients into groups based on their privacy budgets and achieves client-level DP within each group to reduce privacy budget waste.<n>We also introduce GDPFed$+$, which integrates model sparsification to eliminate unnecessary noise and optimize per-group client sampling ratios.
  arXiv  Detail & Related papers  (2025-05-19T18:55:34Z)
• Trust Driven On-Demand Scheme for Client Deployment in Federated Learning [39.9947471801304]
  "Trusted-On-Demand-FL" establishes a relationship of trust between the server and the pool of eligible clients. Our simulations rely on a continuous user behavior dataset, deploying an optimization model powered by a genetic algorithm.
  arXiv  Detail & Related papers  (2024-05-01T08:50:08Z)
• Personalized Federated Learning with Attention-based Client Selection [57.71009302168411]
  We propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions. Experiments on CIFAR10 and FMNIST validate FedACS's superiority.
  arXiv  Detail & Related papers  (2023-12-23T03:31:46Z)
• FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation [69.75513501757628]
  FedGT is a novel framework for identifying malicious clients in federated learning with secure aggregation. We show that FedGT significantly outperforms the private robust aggregation approach based on the geometric median recently proposed by Pillutla et al.
  arXiv  Detail & Related papers  (2023-05-09T14:54:59Z)
• Active Membership Inference Attack under Local Differential Privacy in Federated Learning [18.017082794703555]
  Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection. We propose a new active membership inference (AMI) attack carried out by a dishonest server in FL.
  arXiv  Detail & Related papers  (2023-02-24T15:21:39Z)
• FedABC: Targeting Fair Competition in Personalized Federated Learning [76.9646903596757]
  Federated learning aims to collaboratively train models without accessing their client's local private data. We propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the one-vs-all'' training strategy in each client to alleviate the unfair competition between classes.
  arXiv  Detail & Related papers  (2023-02-15T03:42:59Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM [62.62684911017472]
  Federated learning (FL) enables devices to jointly train shared models while keeping the training data local for privacy purposes. We introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account. VIM achieves significantly higher performance and faster convergence compared with the state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-20T23:14:33Z)
• PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy [14.678119872268198]
  Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Existing FL protocol designs have been shown to be vulnerable to attacks that aim to compromise data privacy and/or model robustness. We develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography.
  arXiv  Detail & Related papers  (2021-10-22T04:08:42Z)

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.