Source code for the paper "Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks".
[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.
Random Client Selection FedAvg Federated 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.
[KDD 2025] Proxy-Validated Importance-Aware Federated Sample Selection with Meta Learning
Code regarding the Semantic Segmentation in Federated Learning project for the Machine Learning and Deep Learning 2022/2023 project.
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