Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting

• URL: http://arxiv.org/abs/2402.15070v1
• Date: Fri, 23 Feb 2024 03:15:10 GMT
• Title: Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
• Authors: Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han
• Abstract summary: One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. We introduce a novel framework, Co-Boosting, in which synthesized data and the ensemble model mutually enhance each other progressively.
• Score: 76.64235084279292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server model is aggregated by distilling knowledge from all client models (the ensemble), which are also responsible for synthesizing samples for distillation. In this regard, advanced works show that the performance of the server model is intrinsically related to the quality of the synthesized data and the ensemble model. To promote OFL, we introduce a novel framework, Co-Boosting, in which synthesized data and the ensemble model mutually enhance each other progressively. Specifically, Co-Boosting leverages the current ensemble model to synthesize higher-quality samples in an adversarial manner. These hard samples are then employed to promote the quality of the ensemble model by adjusting the ensembling weights for each client model. Consequently, Co-Boosting periodically achieves high-quality data and ensemble models. Extensive experiments demonstrate that Co-Boosting can substantially outperform existing baselines under various settings. Moreover, Co-Boosting eliminates the need for adjustments to the client's local training, requires no additional data or model transmission, and allows client models to have heterogeneous architectures.

Related papers

• Balancing Similarity and Complementarity for Federated Learning [91.65503655796603]
  Federated Learning (FL) is increasingly important in mobile and IoT systems. One key challenge in FL is managing statistical heterogeneity, such as non-i.i.d. data. We introduce a novel framework, textttFedSaC, which balances similarity and complementarity in FL cooperation.
  arXiv  Detail & Related papers  (2024-05-16T08:16:19Z)
• 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)
• DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning [13.393529840544117]
  Existing personalized federated learning models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. We propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem.
  arXiv  Detail & Related papers  (2024-03-14T11:12:10Z)
• FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning [15.39242780506777]
  We propose a scalable and diversity-enhanced federated distillation scheme, FedSDD, for federated learning. FedSDD decouples the training complexity from the number of clients to enhance the scalability, and builds the ensemble from a set of aggregated models. Experiment results show that FedSDD outperforms other FL methods, including FedAvg and FedDF, on the benchmark datasets.
  arXiv  Detail & Related papers  (2023-12-28T14:10:00Z)
• 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)
• Heterogeneous Federated Learning via Personalized Generative Networks [7.629157720712401]
  Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. We propose a method for knowledge transfer between clients where the server trains client-specific generators.
  arXiv  Detail & Related papers  (2023-08-25T09:37:02Z)
• Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning [2.1044900734651626]
  We show how the chosen approach for training custom client models has an impact on the global model. We propose a new approach that combines KD and Learning without Forgetting (LwoF) to produce improved personalised models.
  arXiv  Detail & Related papers  (2022-11-07T11:12:57Z)
• 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)
• 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)

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.