Joint Graph Estimation and Signal Restoration for Robust Federated Learning

• URL: http://arxiv.org/abs/2505.11648v1
• Date: Fri, 16 May 2025 19:17:59 GMT
• Title: Joint Graph Estimation and Signal Restoration for Robust Federated Learning
• Authors: Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka,
• Abstract summary: We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications.<n>We show that the proposed method outperforms existing approaches by up to $2$-$5$ in classification accuracy under biased data and noisy conditions.
• Score: 11.817062392718807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central server aggregates local model parameters from multiple clients. These parameters are often noisy and/or have missing values during data collection, training, and communication between the clients and server. This may cause a considerable drop in model accuracy. To address this issue, we learn a graph that represents pairwise relationships between model parameters of the clients during aggregation. We realize it with a joint problem of graph learning and signal (i.e., model parameters) restoration. The problem is formulated as a difference-of-convex (DC) optimization, which is efficiently solved via a proximal DC algorithm. Experimental results on MNIST and CIFAR-10 datasets show that the proposed method outperforms existing approaches by up to $2$--$5\%$ in classification accuracy under biased data distributions and noisy conditions.

Related papers

• 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)
• 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)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs [19.130197923214123]
  Learning an effective global model on private and decentralized datasets has become an increasingly important challenge of machine learning. We propose a feature fusion approach that extracts local representations from local models and incorporates them into a global representation that improves the prediction performance. This paper presents solutions to these problems and demonstrates them in real-world applications on time series data such as power grids and traffic networks.
  arXiv  Detail & Related papers  (2023-06-02T02:24:27Z)
• One-shot Federated Learning without Server-side Training [42.59845771101823]
  One-shot federated learning is gaining popularity as a way to reduce communication cost between clients and the server. Most of the existing one-shot FL methods are based on Knowledge Distillation; however, distillation based approach requires an extra training phase and depends on publicly available data sets or generated pseudo samples. In this work, we consider a novel and challenging cross-silo setting: performing a single round of parameter aggregation on the local models without server-side training.
  arXiv  Detail & Related papers  (2022-04-26T01:45:37Z)
• Layer-wise Adaptive Model Aggregation for Scalable Federated Learning [11.669431684184536]
  In Federated Learning, a common approach for aggregating local models across clients is periodic averaging of the full model parameters. We propose FedLAMA, a layer-wise model aggregation scheme for scalable Federated Learning.
  arXiv  Detail & Related papers  (2021-10-19T22:49:04Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing [113.52575069030192]
  Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
  arXiv  Detail & Related papers  (2020-10-02T10:41:59Z)
• Ensemble Distillation for Robust Model Fusion in Federated Learning [72.61259487233214]
  Federated Learning (FL) is a machine learning setting where many devices collaboratively train a machine learning model. In most of the current training schemes the central model is refined by averaging the parameters of the server model and the updated parameters from the client side. We propose ensemble distillation for model fusion, i.e. training the central classifier through unlabeled data on the outputs of the models from the clients.
  arXiv  Detail & Related papers  (2020-06-12T14:49:47Z)
• Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters [5.319361976450981]
  Federated learning is a distributed, privacy-aware learning scenario which trains a single model on data belonging to several clients. Current federated learning methods struggle in cases with heterogeneous client-side data distributions. We propose a novel representation matching scheme that reduces the divergence of local models.
  arXiv  Detail & Related papers  (2019-12-30T20:19:20Z)

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.