Source code for the paper "Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks". This paper is pulished in IEEE TMC.
[ACM e-Energy'24] Code and data for "FedZero: Leveraging Renewable Excess Energy in Federated Learning"
Source code for the paper "Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks", this paper is pulished in ICC 2024.
[KDD 2025] Proxy-Validated Importance-Aware Federated Sample Selection with Meta Learning
This repository explores Federated Learning (FL) with a focus on FedAvg, client heterogeneity, and novel client selection strategies. We conduct experiments using CIFAR-100 and Shakespeare datasets with PyTorch.
Random Client Selection FedAvg Federated Learning
Code regarding the Semantic Segmentation in Federated Learning project for the Machine Learning and Deep Learning 2022/2023 project.
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