# Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions

arXiv: https://arxiv.org/abs/2012.06810 · HTML: https://mlsanity.com/paper/2012.06810/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 25 of 25)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2206.04621/) (2022)
- [Defending against the Label-flipping Attack in Federated Learning](https://mlsanity.com/paper/2207.01982/) (2022)
- [FL-Defender: Combating Targeted Attacks in Federated Learning](https://mlsanity.com/paper/2207.00872/) (2022)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [FedRight: An Effective Model Copyright Protection for Federated Learning](https://mlsanity.com/paper/2303.10399/) (2023)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [Secure and Privacy-Preserving Federated Learning via Co-Utility](https://mlsanity.com/paper/2108.01913/) (2021)
- [Enhanced Security and Privacy via Fragmented Federated Learning](https://mlsanity.com/paper/2207.05978/) (2022)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency](https://mlsanity.com/paper/2206.05406/) (2022)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [Tackling Selfish Clients in Federated Learning](https://mlsanity.com/paper/2407.15402/) (2024)
- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering](https://mlsanity.com/paper/2409.10392/) (2024)
- [Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders](https://mlsanity.com/paper/2505.00216/) (2025)
- [ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning](https://mlsanity.com/paper/2601.22302/) (2026)
- [Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation](https://mlsanity.com/paper/2603.08735/) (2026)
- [Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation](https://mlsanity.com/paper/2506.12106/) (2025)
- [ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning](https://mlsanity.com/paper/2509.13739/) (2025)
- [Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Health](https://mlsanity.com/paper/2603.06646/) (2026)
- [Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges](https://mlsanity.com/paper/2607.13045/) (2026)

## Abstract (arXiv)

Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server. However, FL is not free of issues. On the one hand, the model updates sent by the clients at each training epoch might leak information on the clients' private data. On the other hand, the model learnt by the server may be subjected to attacks by malicious clients; these security attacks might poison the model or prevent it from converging. In this paper, we first examine security and privacy attacks to FL and critically survey solutions proposed in the literature to mitigate each attack. Afterwards, we discuss the difficulty of simultaneously achieving security and privacy protection. Finally, we sketch ways to tackle this open problem and attain both security and privacy.