Federated Learning with Feedback Alignment

• URL: http://arxiv.org/abs/2512.12762v1
• Date: Sun, 14 Dec 2025 16:59:55 GMT
• Title: Federated Learning with Feedback Alignment
• Authors: Incheol Baek, Hyungbin Kim, Minseo Kim, Yon Dohn Chung,
• Abstract summary: Federated Learning (FL) enables collaborative training across multiple clients while preserving data privacy.<n>We introduce Federated Learning with Feedback Alignment (FLFA), a novel framework that integrates feedback alignment into FL.<n>FLFA uses the global model's weights as a shared feedback matrix during local training's backward pass, aligning local updates with the global model efficiently.
• Score: 1.879054346828946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) enables collaborative training across multiple clients while preserving data privacy, yet it struggles with data heterogeneity, where clients' data are not distributed independently and identically (non-IID). This causes local drift, hindering global model convergence. To address this, we introduce Federated Learning with Feedback Alignment (FLFA), a novel framework that integrates feedback alignment into FL. FLFA uses the global model's weights as a shared feedback matrix during local training's backward pass, aligning local updates with the global model efficiently. This approach mitigates local drift with minimal additional computational cost and no extra communication overhead. Our theoretical analysis supports FLFA's design by showing how it alleviates local drift and demonstrates robust convergence for both local and global models. Empirical evaluations, including accuracy comparisons and measurements of local drift, further illustrate that FLFA can enhance other FL methods demonstrating its effectiveness.

Related papers

• FedNSAM:Consistency of Local and Global Flatness for Federated Learning [26.41380732455181]
  We propose a novel textbfFedNSAM algorithm that accelerates the SAM algorithm by introducing global Nesterov momentum into the local update.<n>textbfFedNSAM uses the global Nesterov momentum as the direction of local estimation of client global perturbations and extrapolation.<n> Empirically, we conduct comprehensive experiments on CNN and Transformer models to verify the superior performance and efficiency of textbfFedNSAM.
  arXiv  Detail & Related papers  (2026-02-27T09:07:47Z)
• FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning [0.0]
  FedDPC is a novel FL method designed to improve FL training and global model performance.<n>We show that FedDPC outperforms state-of-the-art FL algorithms by achieving faster reduction in training loss and improved test accuracy across communication rounds.
  arXiv  Detail & Related papers  (2025-12-23T12:57:27Z)
• Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? [50.03434441234569]
  Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. While various algorithms have shown that FL with local updates is a communication-efficient distributed learning framework, the generalization performance of FL with local updates has received comparatively less attention.
  arXiv  Detail & Related papers  (2024-09-05T19:00:18Z)
• FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation [15.298650496155508]
  Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. Existing FL methods face challenges when dealing with scarce and label-skewed data across devices, resulting in local model overfitting and drift. We propose a pioneering framework called FLea, incorporating the following key components.
  arXiv  Detail & Related papers  (2024-06-13T19:28:08Z)
• 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)
• FedImpro: Measuring and Improving Client Update in Federated Learning [77.68805026788836]
  Federated Learning (FL) models often experience client drift caused by heterogeneous data. We present an alternative perspective on client drift and aim to mitigate it by generating improved local models.
  arXiv  Detail & Related papers  (2024-02-10T18:14:57Z)
• Tunable Soft Prompts are Messengers in Federated Learning [55.924749085481544]
  Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources. The lack of model privacy protection in FL becomes an unneglectable challenge. We propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts.
  arXiv  Detail & Related papers  (2023-11-12T11:01:10Z)
• Rethinking Client Drift in Federated Learning: A Logit Perspective [125.35844582366441]
  Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. We find that the difference in logits between the local and global models increases as the model is continuously updated. We propose a new algorithm, named FedCSD, a Class prototype Similarity Distillation in a federated framework to align the local and global models.
  arXiv  Detail & Related papers  (2023-08-20T04:41:01Z)
• Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning [9.975023463908496]
  Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. We propose a novel regularization technique based on adaptive self-distillation (ASD) for training models on the client side. Our regularization scheme adaptively adjusts to the client's training data based on the global model entropy and the client's label distribution.
  arXiv  Detail & Related papers  (2023-05-31T07:00:42Z)
• 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)
• FedMR: Fedreated Learning via Model Recombination [7.404225808071622]
  Federated Learning (FL) enables global model training across clients without compromising their confidential local data. Existing FL methods rely on Federated Averaging (FedAvg)-based aggregation. This paper proposes a novel and effective FL paradigm named FedMR (Federating Model Recombination)
  arXiv  Detail & Related papers  (2022-08-16T11:30:19Z)

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.