# On Safeguarding Privacy and Security in the Framework of Federated Learning

arXiv: https://arxiv.org/abs/1909.06512 · HTML: https://mlsanity.com/paper/1909.06512/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 30 of 55)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Towards Fair Federated Learning with Zero-Shot Data Augmentation](https://mlsanity.com/paper/2104.13417/) (2021)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering](https://mlsanity.com/paper/2109.05872/) (2021)
- [Low-Latency Federated Learning over Wireless Channels with Differential Privacy](https://mlsanity.com/paper/2106.13039/) (2021)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Federated Learning from Small Datasets](https://mlsanity.com/paper/2110.03469/) (2021)
- [Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning](https://mlsanity.com/paper/2303.04274/) (2023)
- [Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing](https://mlsanity.com/paper/2203.09670/) (2022)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design](https://mlsanity.com/paper/2105.06256/) (2021)
- [G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering](https://mlsanity.com/paper/2306.04984/) (2023)
- [One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity](https://mlsanity.com/paper/2404.12130/) (2024)
- [MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning](https://mlsanity.com/paper/2312.00051/) (2023)
- [Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization](https://mlsanity.com/paper/2101.11799/) (2021)
- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)
- [Federated Stochastic Primal-dual Learning with Differential Privacy](https://mlsanity.com/paper/2204.12284/) (2022)
- [Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning](https://mlsanity.com/paper/2310.05696/) (2023)
- [Secure Federated Learning for Cognitive Radio Sensing](https://mlsanity.com/paper/2304.06519/) (2023)

## Abstract (arXiv)

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding data leakage from the UEs, thereby preserving privacy and security to some extend. However, even if raw data are not disclosed from UEs, individual's private information can still be extracted by some recently discovered attacks in the FL architecture. In this work, we analyze the privacy and security issues in FL, and raise several challenges on preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to illustrate the discussed issues and possible solutions.