Implementation of asynchronous federated learning in flower.

Federated Learning framework extending the nnUNet

This project introduces a system that utilizes the Flower framework, along with the ESP32 microcontroller and the TinyDB database, for stress classification. The system collects and processes real-time biomarker data, enabling local model training on edge devices.

A federated learning-based intrusion detection system designed for securing edge IoT networks through decentralized anomaly detection and privacy-preserving intelligence sharing.

Federated Learning in Satellite Constellations using Flower

Asynchronous Byzantine-robust Federated Learning system for pathology classification with differential privacy and defense filters against poisoning attacks.

Federated Learning simulation using Flower with decentralized client training, secure aggregation concepts, and SHA-256 audit logging.

Sovereign Map is a production-grade, Byzantine-tolerant Federated Learning framework. Utilizing the Mohawk Protocol for streaming aggregation, it achieves a 224x memory reduction, enabling secure orchestration of 100M+ nodes via TPM 2.0 hardware-rooted trust. Features full-stack observability with Prometheus & Grafana, built-in tokenomics telemetry

Federated Learning with 1D-CNN for Web Attack Detection on Edge-IIoTset using the Flower Framework. This project explores both IID and Non-IID data partitions to evaluate federated performance in decentralized IoT environments.

Federated Learning-based Intrusion Detection System for Smart Energy IoT environments with adversarial defense using PyTorch and Flower.

Secure Federated Learning system with Byzantine attack detection, trust scoring, and real-time SOC dashboard. Built with Flower (flwr), PyTorch, FastAPI, and Next.js. Final Year Project — Bahria University 2026.

federated learning framework built with Flower and PyTorch to evaluate the robustness of FL systems under data poisoning attacks.

Privacy-preserving phishing email detection using Federated Learning (BiLSTM + GloVe) with Byzantine-tolerant defense against label flipping attacks.

Federated Learning IDS — Privacy Attack Analysis

Privacy-preserving healthcare AI for global oncology research. Features policy-gated federated learning, HIPAA/GDPR compliance evidence, and a comprehensive research dashboard.

This repository provides a comprehensive solution and codebase for the migration from centralized to federated learning. It demonstrates centralized training, its drawbacks, and how federated learning addresses these issues. It also serves as a tutorial to guide users through the transition process.

Privacy-preserving federated learning for NIH Chest X-ray classification. Demonstrates distributed AI that respects patient privacy by training models locally at hospitals and sharing only model updates.

Prototype for Byzantine-robust Federated Learning using Flower framework

This repository contains the code for the the research project submitted to be able to graduate in masters of computing.

Differential Privacy & Gradient Defense in Secure Federated Averaging — MSc Thesis, ELTE University