Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates

• URL: http://arxiv.org/abs/2107.01477v1
• Date: Sat, 3 Jul 2021 18:48:11 GMT
• Title: Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
• Authors: Zhuohang Li, Luyang Liu, Jiaxin Zhang, Jian Liu
• Abstract summary: Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client. In this paper, we propose to mitigate these failures and attacks from a spatial-temporal perspective. Specifically, we use a clustering-based method to detect and exclude incorrect updates by leveraging their geometric properties in the parameter space.
• Score: 6.758334200305236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client. Compared to traditional centralized machine learning, FL offers many favorable features such as offloading operations which would usually be performed by a central server and reducing risks of serious privacy leakage. However, Byzantine clients that send incorrect or disruptive updates due to system failures or adversarial attacks may disturb the joint learning process, consequently degrading the performance of the resulting model. In this paper, we propose to mitigate these failures and attacks from a spatial-temporal perspective. Specifically, we use a clustering-based method to detect and exclude incorrect updates by leveraging their geometric properties in the parameter space. Moreover, to further handle malicious clients with time-varying behaviors, we propose to adaptively adjust the learning rate according to momentum-based update speculation. Extensive experiments on 4 public datasets demonstrate that our algorithm achieves enhanced robustness comparing to existing methods under both cross-silo and cross-device FL settings with faulty/malicious clients.

Related papers

• Formal Logic-guided Robust Federated Learning against Poisoning Attacks [6.997975378492098]
  Federated Learning (FL) offers a promising solution to the privacy concerns associated with centralized Machine Learning (ML) FL is vulnerable to various security threats, including poisoning attacks, where adversarial clients manipulate the training data or model updates to degrade overall model performance. We present a defense mechanism designed to mitigate poisoning attacks in federated learning for time-series tasks.
  arXiv  Detail & Related papers  (2024-11-05T16:23:19Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees [18.24213566328972]
  Decentralized decentralized learning (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are carried out by the clients without a central server. DSpodFL consistently achieves speeds compared with baselines under various system settings.
  arXiv  Detail & Related papers  (2024-02-05T19:02:19Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
  Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server. Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples. We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
  arXiv  Detail & Related papers  (2023-09-14T03:48:27Z)
• Robust Quantity-Aware Aggregation for Federated Learning [72.59915691824624]
  Malicious clients can poison model updates and claim large quantities to amplify the impact of their model updates in the model aggregation. Existing defense methods for FL, while all handling malicious model updates, either treat all quantities benign or simply ignore/truncate the quantities of all clients. We propose a robust quantity-aware aggregation algorithm for federated learning, called FedRA, to perform the aggregation with awareness of local data quantities.
  arXiv  Detail & Related papers  (2022-05-22T15:13:23Z)
• Over-The-Air Federated Learning under Byzantine Attacks [43.67333971183711]
  Federated learning (FL) is a promising solution to enable many AI applications. FL allows the clients to participate in the training phase, governed by a central server, without sharing their local data. One of the main challenges of FL is the communication overhead. We propose a transmission and aggregation framework to reduce the effect of such attacks.
  arXiv  Detail & Related papers  (2022-05-05T22:09:21Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Decentralized Federated Learning via Mutual Knowledge Transfer [37.5341683644709]
  Decentralized federated learning (DFL) is a problem in the Internet of things (IoT) systems. We propose a mutual knowledge transfer (Def-KT) algorithm where local clients fuse models by transferring their learnt knowledge to each other. Our experiments on the MNIST, Fashion-MNIST, and CIFAR10 datasets reveal datasets that the proposed Def-KT algorithm significantly outperforms the baseline DFL methods.
  arXiv  Detail & Related papers  (2020-12-24T01:43:53Z)
• A Framework for Evaluating Gradient Leakage Attacks in Federated Learning [14.134217287912008]
  Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients. Recent studies have shown that even sharing local parameter updates from a client to the federated server may be susceptible to gradient leakage attacks. We present a principled framework for evaluating and comparing different forms of client privacy leakage attacks.
  arXiv  Detail & Related papers  (2020-04-22T05:15:03Z)

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.