client-selection

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

A robust Federated Learning framework implementing the novel FedCADS-UCB algorithm. Engineered for resilient client selection using CUSUM drift detection, adaptive multi-armed bandits, and hierarchical clustering to maintain high accuracy (>95%) during concept drift and label poisoning attacks.

This artifact provides a complete and runnable implementation of a federated learning client selection system inspired by DCS (Accepted in IEEE PerCom.

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.

Federated Learning with client selection strategies (Random, Loss-based, Power-of-Choice), domain generalisation and model pruning. Applied to image classification (FEMNIST) and semantic segmentation (IDDA). PyTorch.

Code regarding the Semantic Segmentation in Federated Learning project for the Machine Learning and Deep Learning 2022/2023 project.

A PyTorch framework for drift-resilient Federated Learning using Shapley values, UCB bandits, and CUSUM drift detection (FedCADS-UCB).

Energy-aware federated learning framework for heterogeneous IoT — joint computation & communication optimization achieving a 59% reduction in total energy vs. FedAvg baseline while maintaining ~91-92% accuracy (Flower + PyTorch, MNIST)