Securing Federated Machine Learning: Kick-off!

Ready to deep dive into the Federated Learning journey with selected state-of-the-art and valuable readings!

🎯 Intro


  1. Google – Federated Learning: Collaborative Machine Learning without Centralized Training Data – April 6, 2017
  2. Comic –

Initial papers

  1. Practical Secure Aggregation for Privacy-Preserving Machine Learning – Google, 2016
  2. Federated Learning: Strategies for Improving Communication Efficiency – 2016
  3. Communication-Efficient Learning of Deep Networks from Decentralized Data – 2017

Talk – Seminar

  1. Federated Learning One World Seminar –
  2. Coursera –

📜 Survey

  1. Advances and Open Problems in Federated Learning
  2. A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
  3. A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
  4. Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
  5. Threats to Federated Learning: A Survey
  6. Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
  7. Federated Learning: Challenges, Methods, and Future Directions
  8. Federated Learning in Mobile Edge Networks: A Comprehensive Survey
  9. Federated Machine Learning: Concept and Applications
  10. Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
  11. A Review of Privacy-preserving Federated Learning for the Internet-of-Things

📦 System design – frameworks – libraries

  1. PySyft – A library for computing on data you do not own and cannot see
  2. Tensorflow Federated
  3. FedML: A Research Library and Benchmark for Federated Machine Learning
  4. Flower – A Friendly Federated Learning Framework
  5. Federated Learning Pytorch
  6. PrivacyFL: A simulator for privacy-preserving and secure federated learning.
  7. Towards Federated Learning at Scale: System Design

💻 Models and Applications

  1. DIOT: A Federated Self-learning Anomaly Detection System for IoT
  2. MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets.
  3. (GAN) Federated Generative Adversarial Learning
  4. Efficient Privacy-Preserving Edge Computing Framework for Image Classification

🛡️ Security and Privacy


An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies. 2020-04-01 Citation: 0

Backdoor Attacks

Awesome Backdoor Learning List:
Survey: Backdoor Learning – a survey

  1. AISTATS 2020 – How To Backdoor Federated Learning ✅ ⭐️⭐️
  2. Blind Backdoors in Deep Learning Models (2021) ✅ ⭐️⭐️
  3. ICLR 2020 – DBA: Distributed Backdoor Attacks against Federated Learning
  4. NeurIPS 2019 – Can You Really Backdoor Federated Learning?
  5. Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
  6. NeurIPS 2020 Submission: Backdoor Attacks on Federated Meta-Learning

Data Poisoning

  1. Data Poisoning Attacks Against Federated Learning Systems ✅ ⭐️⭐️
  2. Data Poisoning Attacks on Federated Machine Learning
  3. Poisoning Attacks with Generative Adversarial Nets
  4. Poisoning Attack in Federated Learning using Generative Adversarial Nets

Model Poisoning

  1. ICML 2019 – Analyzing Federated Learning through an Adversarial Lens ✅ ⭐️⭐️⭐️ – Citation: 165 – Highlight: client attack
  2. USS 2020 – Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

Inference Attacks

  1. Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning

Free-rider Attacks

  1. NeurIPS 2020 – Free-rider Attacks on Model Aggregation in Federated Learning
  2. Free-riders in Federated Learning: Attacks and Defenses


  1. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. ACM CCS 2017. 2017-02-14 Citation: 284
  2. Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. INFOCOM 2019 Citation: 56 Highlight: server-side attack
  3. A Framework for Evaluating Gradient Leakage Attacks in Federated Learning. 2020-04-22 Researcher: Wenqi Wei, Ling Liu, GaTech
  4. Quantification of the Leakage in Federated Learning. 2019-10-12 Citation: 1


  1. Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices
  2. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. 2018-12-03 Citation: 46
  3. Inverting Gradients – How easy is it to break privacy in federated learning? 2020-03-31 Citation: 3


  1. Mitigating Sybils in Federated Learning Poisoning. 2018-08-14. RAID 2020 Citation: 41 Highlight: defense
  2. RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets, AAAI 2019 Citation: 34
  3. Towards Realistic Byzantine-Robust Federated Learning. 2020-04-10
  4. FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. 2020-06-28
  5. Learning to Detect Malicious Clients for Robust Federated Learning. 2020-02-01
  6. Robust Aggregation for Federated Learning. 2019-12-31 Citation: 9
  7. Towards Deep Federated Defenses Against Malware in Cloud Ecosystems. 2019-12-27
  8. Attack-Resistant Federated Learning with Residual-based Reweighting. 2019-12-23
  9. Robust Federated Learning with Noisy Communication. 2019-11-01 Citation:
  10. Abnormal Client Behavior Detection in Federated Learning. 2019-10-22 Citation: 3
  11. Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging. 2019-09-11
  12. An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning. 2019-08-22
  13. Ensemble Distillation for Robust Model Fusion in Federated Learning

Other Resources and References

Leave a Reply

Your email address will not be published.Required fields are marked *