Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph

• URL: http://arxiv.org/abs/2210.00325v1
• Date: Sat, 1 Oct 2022 17:17:22 GMT
• Title: Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph
• Authors: Yang Lu, Zhengxin Yu, Neeraj Suri
• Abstract summary: We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation. The paper establishes the correctness and privacy properties of the proposed algorithm.
• Score: 5.649296652252663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation in an environment of high mobility, where the communication graph between the learners may vary between successive rounds of model aggregation. In particular, in each round of global model aggregation, the Metropolis-Hastings method is applied to update the weighted adjacency matrix based on the current communication topology. In addition, the Shamir's secret sharing scheme is integrated to facilitate privacy in reaching consensus of the global model. The paper establishes the correctness and privacy properties of the proposed algorithm. The computational efficiency is evaluated by a simulation built on a federated learning framework with a real-word dataset.

Related papers

• Proximity-based Self-Federated Learning [1.0066310107046081]
  This paper introduces a novel, fully-distributed federated learning strategy called proximity-based self-federated learning. Unlike traditional algorithms, our approach encourages clients to share and adjust their models with neighbouring nodes based on geographic proximity and model accuracy.
  arXiv  Detail & Related papers  (2024-07-17T08:44:45Z)
• Differentially Private Decentralized Learning with Random Walks [15.862152253607496]
  We characterize the privacy guarantees of decentralized learning with random walk algorithms, where a model is updated by traveling from one node to another along the edges of a communication graph. Our results reveal that random walk algorithms tends to yield better privacy guarantees than gossip algorithms for nodes close from each other.
  arXiv  Detail & Related papers  (2024-02-12T08:16:58Z)
• Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus [11.414398732656839]
  We propose a framework that embeds differential privacy into decentralized deep learning and secures each agent's local dataset during and after cooperative training. We prove convergence guarantees for algorithms derived from this framework and demonstrate its practical utility when applied to subgradient and ADMM decentralized approaches.
  arXiv  Detail & Related papers  (2023-06-24T07:46:00Z)
• FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid [4.241208172557663]
  We propose a two-stage federated learning approach toward the objective of privacy protection. In the first stage, the synthetic dataset is generated by employing two different distributions as noise. In the second stage, the Federated Probabilistic Neural Network (FedPNN) is developed and employed for building globally shared classification model.
  arXiv  Detail & Related papers  (2023-04-09T03:23:37Z)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data [69.0785021613868]
  Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos. We propose the Federated Invariant Learning Consistency (FedILC) approach, which leverages the gradient covariance and the geometric mean of Hessians to capture both inter-silo and intra-silo consistencies. This is relevant to various fields such as medical healthcare, computer vision, and the Internet of Things (IoT)
  arXiv  Detail & Related papers  (2022-05-19T03:32:03Z)
• Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization [57.98426940386627]
  We show that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-02-10T20:44:44Z)
• Decentralised Person Re-Identification with Selective Knowledge Aggregation [56.40855978874077]
  Existing person re-identification (Re-ID) methods mostly follow a centralised learning paradigm which shares all training data to a collection for model learning. Two recent works have introduced decentralised (federated) Re-ID learning for constructing a globally generalised model (server) However, these methods are poor on how to adapt the generalised model to maximise its performance on individual client domain Re-ID tasks. We present a new Selective Knowledge Aggregation approach to decentralised person Re-ID to optimise the trade-off between model personalisation and generalisation.
  arXiv  Detail & Related papers  (2021-10-21T18:09:53Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)
• Privacy-preserving Decentralized Aggregation for Federated Learning [3.9323226496740733]
  Federated learning is a promising framework for learning over decentralized data spanning multiple regions. We develop a privacy-preserving decentralized aggregation protocol for federated learning. We evaluate our algorithm on image classification and next-word prediction applications over benchmark datasets with 9 and 15 distributed sites.
  arXiv  Detail & Related papers  (2020-12-13T23:45:42Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)

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.