# CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy

arXiv: https://arxiv.org/abs/2509.23190 · HTML: https://mlsanity.com/paper/2509.23190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Generalized Byzantine-tolerant SGD](https://mlsanity.com/paper/1802.10116/) (2018)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)
- [Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning](https://mlsanity.com/paper/1806.05358/) (2018)
- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning](https://mlsanity.com/paper/2009.05604/) (2020)
- [FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks](https://mlsanity.com/paper/2308.05832/) (2023)
- [Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice](https://mlsanity.com/paper/2304.07134/) (2023)
- [Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution](https://mlsanity.com/paper/2404.06721/) (2024)
- [Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2502.08151/) (2025)

## Abstract (arXiv)

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.