FedOS: using open-set learning to stabilize training in federated learning
Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between clients and the server. This brings many advantages but also poses new challenges. In this report, we explore this new research area and perform several experiments to deepen our understanding of what these challenges are and how different problem settings affect the performance of the final model. Finally, we present a novel approach to one of these challenges and compare it to other methods found in literature.
Code (1)
Tasks
Federated LearningOpen Set LearningSimilar Papers 제목 키워드 기반
FedPD: Federated Open Set Recognition with Parameter Disentanglement
Existing federated learning (FL) approaches are deployed under the unrealistic closed-set setting, with both training and testing classes belong to the same set, which makes the global model fail to identify the unse…
DisentanglementFederated LearningOpen Set LearningTowards Unbiased Training in Federated Open-world Semi-supervised Learning
Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data. However, …
Open-World Semi-Supervised LearningTransfer LearningFedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data on their own devices. First-order method…
Federated LearningSecond-order methodsFederated Unlearning with Gradient Descent and Conflict Mitigation
Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly remember clients' local data. Therefore, i…
Federated LearningOnline Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem of privacy leakage aroused by centralize…
Federated LearningGraph Attention