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

• URL: http://arxiv.org/abs/2012.06810v1
• Date: Sat, 12 Dec 2020 13:23:56 GMT
• Title: Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
• Authors: Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Mart\'inez, David S\'anchez, Adrian Flanagan and Kuan Eeik Tan
• Abstract summary: Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients. In this paper, we first examine security and privacy attacks to FL and critically survey solutions proposed in the literature to mitigate each attack.
• Score: 6.460846767084875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Federated Learning Privacy: Attacks, Defenses, Applications, and Policy Landscape - A Survey [27.859861825159342]
  Deep learning has shown incredible potential across a vast array of tasks. Recent concerns on privacy have further highlighted challenges for accessing such data. Federated learning has emerged as an important privacy-preserving technology.
  arXiv  Detail & Related papers  (2024-05-06T16:55:20Z)
• Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks [48.70867241987739]
  InferGuard is a novel Byzantine-robust aggregation rule aimed at defending against client-side training data distribution inference attacks. The results of our experiments indicate that our defense mechanism is highly effective in protecting against client-side training data distribution inference attacks.
  arXiv  Detail & Related papers  (2024-03-05T17:41:35Z)
• Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare [0.0]
  We present vulnerabilities, attacks, and defenses based on the widened attack surfaces of Federated Learning. We suggest promising new research directions toward a more robust FL.
  arXiv  Detail & Related papers  (2024-01-16T16:07:53Z)
• FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks [1.689369173057502]
  Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. malicious clients are able to corrupt the global model and degrade performance across all clients within a federation. Our novel aggregation method, FedBayes, mitigates the effect of a malicious client by calculating the probabilities of a client's model weights.
  arXiv  Detail & Related papers  (2023-12-04T21:37:50Z)
• SaFL: Sybil-aware Federated Learning with Application to Face Recognition [13.914187113334222]
  Federated Learning (FL) is a machine learning paradigm to conduct collaborative learning among clients on a joint model. On the downside, FL raises security and privacy concerns that have just started to be studied. This paper proposes a new defense method against poisoning attacks in FL called SaFL.
  arXiv  Detail & Related papers  (2023-11-07T21:06:06Z)
• Mitigating Cross-client GANs-based Attack in Federated Learning [78.06700142712353]
  Multi distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model. FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack. We propose Fed-EDKD technique to improve the current popular FL schemes to resist C-GANs attack.
  arXiv  Detail & Related papers  (2023-07-25T08:15:55Z)
• FedDefender: Client-Side Attack-Tolerant Federated Learning [60.576073964874]
  Federated learning enables learning from decentralized data sources without compromising privacy. It is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. We propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models.
  arXiv  Detail & Related papers  (2023-07-18T08:00:41Z)
• Efficient and Privacy Preserving Group Signature for Federated Learning [2.121963121603413]
  Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. This paper proposes an efficient and privacy-preserving protocol for FL based on group signature.
  arXiv  Detail & Related papers  (2022-07-12T04:12:10Z)
• Do Gradient Inversion Attacks Make Federated Learning Unsafe? [70.0231254112197]
  Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. Recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in real FL use-cases and provide a new baseline attack.
  arXiv  Detail & Related papers  (2022-02-14T18:33:12Z)
• RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
  Federated Learning allows a large number of clients to train a joint model without the need to share their private data. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation. We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
  arXiv  Detail & Related papers  (2021-07-07T15:42:49Z)
• Privacy and Robustness in Federated Learning: Attacks and Defenses [74.62641494122988]
  We conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic.
  arXiv  Detail & Related papers  (2020-12-07T12:11:45Z)

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.