Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information

• URL: http://arxiv.org/abs/2410.05545v1
• Date: Mon, 7 Oct 2024 23:14:05 GMT
• Title: Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information
• Authors: Emanuel Buttaci, Giuseppe Carlo Calafiore,
• Abstract summary: Federated learning has emerged as a distributed optimization paradigm. We propose a novel modified framework wherein each client locally performs a perturbed gradient step. We show that our algorithm speeds convergence up to a margin of 30 global rounds compared with FedAvg.
• Score: 6.767885381740953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning has emerged in the last decade as a distributed optimization paradigm due to the rapidly increasing number of portable devices able to support the heavy computational needs related to the training of machine learning models. Federated learning utilizes gradient-based optimization to minimize a loss objective shared across participating agents. To the best of our knowledge, the literature mostly lacks elegant solutions that naturally harness the reciprocal statistical similarity between clients to redesign the optimization procedure. To address this gap, by conceiving the federated network as a similarity graph, we propose a novel modified framework wherein each client locally performs a perturbed gradient step leveraging prior information about other statistically affine clients. We theoretically prove that our procedure, due to a suitably introduced adaptation in the update rule, achieves a quantifiable speedup concerning the exponential contraction factor in the strongly convex case compared with popular algorithms FedAvg and FedProx, here analyzed as baselines. Lastly, we legitimize our conclusions through experimental results on the CIFAR10 and FEMNIST datasets, where we show that our algorithm speeds convergence up to a margin of 30 global rounds compared with FedAvg while modestly improving generalization on unseen data in heterogeneous settings.

Related papers

• Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration [66.43954501171292]
  We introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. DFedCata consists of two main components: the Moreau envelope function, which addresses parameter inconsistencies, and Nesterov's extrapolation step, which accelerates the aggregation phase. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIFAR10/100 with various non-iid data distributions.
  arXiv  Detail & Related papers  (2024-10-09T06:17:16Z)
• Federated Learning based on Pruning and Recovery [0.0]
  This framework integrates asynchronous learning algorithms and pruning techniques. It addresses the inefficiencies of traditional federated learning algorithms in scenarios involving heterogeneous devices. It also tackles the staleness issue and inadequate training of certain clients in asynchronous algorithms.
  arXiv  Detail & Related papers  (2024-03-16T14:35:03Z)
• 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)
• Tackling Computational Heterogeneity in FL: A Few Theoretical Insights [68.8204255655161]
  We introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneous data. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
  arXiv  Detail & Related papers  (2023-07-12T16:28:21Z)
• FedAgg: Adaptive Federated Learning with Aggregated Gradients [1.5653612447564105]
  We propose an adaptive FEDerated learning algorithm called FedAgg to alleviate the divergence between the local and average model parameters and obtain a fast model convergence rate. We show that our framework is superior to existing state-of-the-art FL strategies for enhancing model performance and accelerating convergence rate under IID and Non-IID datasets.
  arXiv  Detail & Related papers  (2023-03-28T08:07:28Z)
• Adaptive Federated Learning via New Entropy Approach [14.595709494370372]
  Federated Learning (FL) has emerged as a prominent distributed machine learning framework. In this paper, we propose an adaptive FEDerated learning algorithm based on ENTropy theory (FedEnt) to alleviate the parameter deviation among heterogeneous clients.
  arXiv  Detail & Related papers  (2023-03-27T07:57:04Z)
• 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)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Fed-LAMB: Layerwise and Dimensionwise Locally Adaptive Optimization Algorithm [24.42828071396353]
  In the emerging paradigm of federated learning (FL), large amount of clients, such as mobile devices, are used to train on their respective data. Due to the low bandwidth, decentralized optimization methods need to shift the computation burden from those clients to those servers. We present Fed-LAMB, a novel learning method based on a layerwise, deep neural networks.
  arXiv  Detail & Related papers  (2021-10-01T16:54:31Z)
• Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity [57.275753974812666]
  Federated learning involves learning from data samples distributed across a network of clients while the data remains local. In this paper, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure.
  arXiv  Detail & Related papers  (2020-12-28T19:21:14Z)

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.