FedEdge-6G Synthetic Federated Learning Dataset (v1.0)
Year: 2025
Type: article
Abstract: This repository contains the synthetic dataset used in our paper “FedEdge-6G: Cost-Optimised, FLTrust-Hardened Federated Learning for 6G Edge Networks.” The data emulate latency-constrained, non-IID client partitions typical of vehicular and smart-factory environments. Contents /clients/ – 100 JSON files, one per simulated client, each with 10 000 labelled samples (features ≈ channel state + task demand; target ≈ regression value). README.md – schema and generation script parameters. gen_config.yaml – reproducible seed & distribution settings. Intended use Benchmarking federated-learning algorithms under non-IID, bandwidth-limited, and adversarial settings. Ethics / limitations No real user data; entirely synthetic. Not suitable for evaluating privacy leakage on real-world sensitive attributes.
Author: Bilal, Awais
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949755427
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