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Found 6 papers, 1 papers with code
Computation and Communication Efficient Lightweighting Vertical Federated Learning
Moreover, we establish a convergence bound for our LVFL algorithm, which accounts for both communication and computational lightweighting ratios.
Online Vertical Federated Learning for Cooperative Spectrum Sensing
However, deep learning-based CSS methods often rely on centralized learning, posing challenges like communication overhead and data privacy risks.
On the Local Cache Update Rules in Streaming Federated Learning
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity.
Combating Client Dropout in Federated Learning via Friend Model Substitution
Federated learning (FL) is a new distributed machine learning framework known for its benefits on data privacy and communication efficiency.
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients.
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data.
