A Secure Aggregation for Federated Learning on Long-Tailed Data

• URL: http://arxiv.org/abs/2307.08324v1
• Date: Mon, 17 Jul 2023 08:42:21 GMT
• Title: A Secure Aggregation for Federated Learning on Long-Tailed Data
• Authors: Yanna Jiang, Baihe Ma, Xu Wang, Guangsheng Yu, Caijun Sun, Wei Ni, Ren Ping Liu
• Abstract summary: Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. A novel two-layer aggregation method is proposed for the rejection of malicious models and the advisable selection of valuable models. Preliminary experiments validate that the think tank can make effective model selections for global aggregation.
• Score: 26.168909973264707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. In this paper, we consider the long-tailed distribution with the presence of Byzantine nodes in the FL scenario. A novel two-layer aggregation method is proposed for the rejection of malicious models and the advisable selection of valuable models containing tail class data information. We introduce the concept of think tank to leverage the wisdom of all participants. Preliminary experiments validate that the think tank can make effective model selections for global aggregation.

Related papers

• Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics [20.781607752797445]
  We propose a two-stage Decoupled Federated learning framework using Feature Statistics (DFL-FS) In the first stage, the server estimates the client's class coverage distributions through masked local feature statistics clustering. In the second stage, DFL-FS employs federated feature regeneration based on global feature statistics to enhance the model's adaptability to long-tailed data distributions.
  arXiv  Detail & Related papers  (2024-03-13T09:24:59Z)
• Robust Training of Federated Models with Extremely Label Deficiency [84.00832527512148]
  Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. We propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings.
  arXiv  Detail & Related papers  (2024-02-22T10:19:34Z)
• Twice Class Bias Correction for Imbalanced Semi-Supervised Learning [59.90429949214134]
  We introduce a novel approach called textbfTwice textbfClass textbfBias textbfCorrection (textbfTCBC) We estimate the class bias of the model parameters during the training process. We apply a secondary correction to the model's pseudo-labels for unlabeled samples.
  arXiv  Detail & Related papers  (2023-12-27T15:06:36Z)
• Exploiting Label Skews in Federated Learning with Model Concatenation [39.38427550571378]
  Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. Among different non-IID types, label skews have been challenging and common in image classification and other tasks. We propose FedConcat, a simple and effective approach that degrades these local models as the base of the global model.
  arXiv  Detail & Related papers  (2023-12-11T10:44:52Z)
• Ensemble Modeling for Multimodal Visual Action Recognition [50.38638300332429]
  We propose an ensemble modeling approach for multimodal action recognition. We independently train individual modality models using a variant of focal loss tailored to handle the long-tailed distribution of the MECCANO [21] dataset.
  arXiv  Detail & Related papers  (2023-08-10T08:43:20Z)
• 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)
• FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation [54.2658887073461]
  Dealing with non-IID data is one of the most challenging problems for federated learning. This paper studies the joint problem of non-IID and long-tailed data in federated learning and proposes a corresponding solution called Federated Ensemble Distillation with Imbalance (FEDIC) FEDIC uses model ensemble to take advantage of the diversity of models trained on non-IID data.
  arXiv  Detail & Related papers  (2022-04-30T06:17:36Z)
• FedH2L: Federated Learning with Model and Statistical Heterogeneity [75.61234545520611]
  Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. We introduce FedH2L, which is agnostic to both the model architecture and robust to different data distributions across participants. In contrast to approaches sharing parameters or gradients, FedH2L relies on mutual distillation, exchanging only posteriors on a shared seed set between participants in a decentralized manner.
  arXiv  Detail & Related papers  (2021-01-27T10:10:18Z)
• Mitigating Bias in Federated Learning [9.295028968787351]
  In this paper, we discuss causes of bias in federated learning (FL) We propose three pre-processing and in-processing methods to mitigate bias, without compromising data privacy. We conduct experiments over several data distributions to analyze their effects on model performance, fairness metrics, and bias learning patterns.
  arXiv  Detail & Related papers  (2020-12-04T08:04:12Z)

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.