DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning

• URL: http://arxiv.org/abs/2309.13546v2
• Date: Sat, 7 Oct 2023 04:14:01 GMT
• Title: DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
• Authors: Kangyang Luo, Shuai Wang, Yexuan Fu, Xiang Li, Yunshi Lan, Ming Gao
• Abstract summary: Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm. We propose a new FL method (namely DFRD) to learn a robust global model in the data-heterogeneous and model-heterogeneous FL scenarios.
• Score: 20.135235291912185
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm in which clients enable collaborative training without compromising private data. However, how to learn a robust global model in the data-heterogeneous and model-heterogeneous FL scenarios is challenging. To address it, we resort to data-free knowledge distillation to propose a new FL method (namely DFRD). DFRD equips a conditional generator on the server to approximate the training space of the local models uploaded by clients, and systematically investigates its training in terms of fidelity, transferability} and diversity. To overcome the catastrophic forgetting of the global model caused by the distribution shifts of the generator across communication rounds, we maintain an exponential moving average copy of the generator on the server. Additionally, we propose dynamic weighting and label sampling to accurately extract knowledge from local models. Finally, our extensive experiments on various image classification tasks illustrate that DFRD achieves significant performance gains compared to SOTA baselines.

Related papers

• DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning [17.34783038347845]
  Federated Learning (FL) is a distributed machine learning scheme in which clients jointly participate in the collaborative training of a global model. We propose a new data-free dual-generator adversarial distillation method (namely DFDG) for one-shot FL. DFDG is executed in an adversarial manner and comprises two parts: dual-generator training and dual-model distillation.
  arXiv  Detail & Related papers  (2024-09-12T03:44:30Z)
• FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation [32.305134875959226]
  Federated learning (FL) is a privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server. We propose FedHPL, a parameter-efficient unified $textbfFed$erated learning framework for $textbfH$eterogeneous settings. We show that our framework outperforms state-of-the-art FL approaches, with less overhead and training rounds.
  arXiv  Detail & Related papers  (2024-05-27T15:25:32Z)
• 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)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• 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)
• 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)
• The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation [17.570719572024608]
  FedHKD (Federated Hyper-Knowledge Distillation) is a novel FL algorithm in which clients rely on knowledge distillation to train local models. Unlike other KD-based pFL methods, FedHKD does not rely on a public dataset nor it deploys a generative model at the server. We conduct extensive experiments on visual datasets in a variety of scenarios, demonstrating that FedHKD provides significant improvement in both personalized as well as global model performance.
  arXiv  Detail & Related papers  (2023-01-21T16:20:57Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• Data-Free Knowledge Distillation for Heterogeneous Federated Learning [31.364314540525218]
  Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data. Knowledge Distillation has recently emerged to tackle this issue, by refining the server model using aggregated knowledge from heterogeneous users. We propose a data-free knowledge distillation approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner.
  arXiv  Detail & Related papers  (2021-05-20T22:30:45Z)

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.