Nothing Special   »   [go: up one dir, main page]

×
Please click here if you are not redirected within a few seconds.
Nov 9, 2023 · This paper first proposes a fairness-based asynchronous federated learning mechanism, which reduces the adverse effects of device and data heterogeneity on the ...
Nov 9, 2023 · This paper first proposes a fairness-based asynchronous federated learning mechanism, which reduces the adverse effects of device and data ...
Federated learning is a mechanism for model training in distributed systems, aiming to protect data privacy while achieving collective intelligence.
Federated learning is a mechanism for model training in distributed systems, aiming to protect data privacy while achieving collective intelligence.
People also ask
Nov 9, 2024 · Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models ...
Oct 10, 2023 · In this study, we propose a novel asynchronous FL framework that integrates an incentive mechanism based on contract theory.
Missing: fairness- | Show results with:fairness-
Personalized federated learning refers to train a model for each client, based on the client's own dataset and the datasets of other clients. There are two ...
Jul 20, 2023 · We propose the Fairness-aware Federated Client Selection (FairFedCS) approach. Based on Lyapunov optimization, it dynamically adjusts FL clients' selection ...
Missing: asynchronous mechanism.
Oct 13, 2024 · In this study, FFL discusses the causes of bias and fairness of existing FL, and separates solutions based on data partitioning strategies, privacy mechanisms.
Nov 14, 2023 · We propose a contribution-based differentiated global model mechanism to address the fairness challenge in FL. This mechanism ensures that high- ...