Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation

• URL: http://arxiv.org/abs/2403.14905v1
• Date: Fri, 22 Mar 2024 01:51:48 GMT
• Title: Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
• Authors: Chengxi Li, Ming Xiao, Mikael Skoglund,
• Abstract summary: A coded federated learning framework has been proposed to mitigate the negative impact of stragglers. We propose a new method named adaptive coded federated learning (ACFL) to overcome this drawback.
• Score: 33.56146654796337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to mitigate the negative impact of the stragglers. However, when aggregating these gradients, fixed weights are consistently applied across iterations, neglecting the generation process of the global coded dataset and the dynamic nature of the trained model over iterations. This oversight may result in diminished learning performance. To overcome this drawback, we propose a new method named adaptive coded federated learning (ACFL). In ACFL, before the training, each device uploads a coded local dataset with additive noise to the central server to generate a global coded dataset under privacy preservation requirements. During each iteration of the training, the central server aggregates the gradients received from the non-stragglers and the gradient computed from the global coded dataset, where an adaptive policy for varying the aggregation weights is designed. Under this policy, we optimize the performance in terms of privacy and learning, where the learning performance is analyzed through convergence analysis and the privacy performance is characterized via mutual information differential privacy. Finally, we perform simulations to demonstrate the superiority of ACFL compared with the non-adaptive methods.

Related papers

• On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients [2.2530496464901106]
  The holy grail of machine learning is to enable Continual Federated Learning (CFL) to enhance the efficiency, privacy, and scalability of AI systems while learning from streaming data. We propose a novel replay-memory based federated strategy consisting of edge-based gradient updates on memory and aggregated gradients on the current data. We empirically show that C-FLAG outperforms several state-of-the-art baselines on both task and class-incremental settings with respect to metrics such as accuracy and forgetting.
  arXiv  Detail & Related papers  (2024-11-12T17:36:20Z)
• On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning [4.322339935902437]
  Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. We propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning.
  arXiv  Detail & Related papers  (2024-10-28T16:35:40Z)
• FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning [57.38427653043984]
  Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients. We introduce FedCAda, an innovative federated client adaptive algorithm designed to tackle this challenge. We demonstrate that FedCAda outperforms the state-of-the-art methods in terms of adaptability, convergence, stability, and overall performance.
  arXiv  Detail & Related papers  (2024-05-20T06:12:33Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization [21.825321314169642]
  We propose a federated learning text summarization scheme, which allows users to share the global model in a cooperative learning manner without sharing raw data. FedSUMM can achieve faster model convergence on PFL algorithm for task-specific text summarization.
  arXiv  Detail & Related papers  (2023-04-23T03:18:46Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning [60.41501515192088]
  Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively. The data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model. In this work, we integrate the local real data with the global gradient prototypes to form the local balanced datasets.
  arXiv  Detail & Related papers  (2023-01-25T03:18:10Z)
• Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design [18.675244280002428]
  We propose a novel FL framework named coded federated learning (SCFL) that leverages coded computing techniques. In SCFL, each edge device uploads a privacy-preserving coded dataset to the server, which is generated by adding noise to the projected local dataset. We show that SCFL learns a better model within the given time and achieves a better privacy-performance tradeoff than the baseline methods.
  arXiv  Detail & Related papers  (2022-11-08T09:58:36Z)
• 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)
• Stochastic Coded Federated Learning with Convergence and Privacy Guarantees [8.2189389638822]
  Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework. This paper proposes a coded federated learning framework, namely coded federated learning (SCFL) to mitigate the straggler issue. We characterize the privacy guarantee by the mutual information differential privacy (MI-DP) and analyze the convergence performance in federated learning.
  arXiv  Detail & Related papers  (2022-01-25T04:43:29Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)

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.