F

federated learning

End-to-end ns-3.43 simulator for 6G non-terrestrial networks. Five integrated modules: 3GPP Rel-17 TTE-aware Conditional Handover, O-RAN Near-RT + Space RIC with 13 xApps, sub-THz/THz physics (HITRAN, UM-MIMO, RIS, ISAC), ns3-ai fork with federated learning, NTN traffic — over SNS3, mmWave-NR, 3GPP TR 38.811. LEO/MEO/GEO.

Space O-RAN reference implementation for ns-3.43: Near-RT RIC + Space RIC, 13 xApps, A1 policy engine (11 policies), E2SM-RC runtime (28 actions), 5 conflict-resolution strategies and 4 federated-learning aggregators (FedAvg/Prox/Nova/SCAFFOLD). Validated 60-s LEO run: 7369 actions, 0 conflicts. GPL-2.0.

Modernised fork of ns3-ai (Yin et al., WNS3 2020): ns-3.43 + Python 3.13 + NumPy 2 + Gymnasium 1.0 compatibility patches for the shared-memory C++/Python bridge. Adds NTN Gym environments and a Flower AI federated-learning adaptor (FedAvg/Prox/Nova/SCAFFOLD). Upstream credit preserved in AUTHORS.md. GPL-2.0.

Federated learning client for 2-year lung cancer survival prediction using TNM staging data. Built on BranchKey FL platform.

Repository for the Bachelor project on initialization strategies for federated k-means for cross-silo federated learning.

An example SCOP Framework project for simulating federated learning using federated averaging.

An example Python project for simulating federated learning using federated averaging.

This GitLab repository serves as a dedicated space for conducting experiments and validations related to the research paper titled 'Federated learning efficient communication: Sparse single layer updates' submitted to the conference ISGTA'2023 (International Symposium on Green Technologies and Applications). You will find code notebook used to conduct our research validation here.

Federated learning project. Using python , pytorch , Flower , Mlflow , Docker Create architecture to create , deploy model with Flower( binary classification) .