Implementation of asynchronous federated learning in flower.

Federated Learning framework extending the nnUNet

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 in Satellite Constellations using Flower

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

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

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

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

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.

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

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

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

Federated Learning IDS — Privacy Attack Analysis

Federated learning sentiment analysis with AT-FedAvg — adaptive trust-aware aggregation across IMDB, Sentiment140 & Amazon. BiLSTM · PyTorch · Flower · FastAPI · Streamlit

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

Privacy-preserving Federated Learning Intrusion Detection System for heterogeneous IoT networks with Differential Privacy and adversarial robustness.

This API caters to data scientists, simplifying remote host communication with service endpoints. It allows users to efficiently manage flower federated learning clusters.

Privacy-preserving medical diagnosis system using Federated Learning (Flower), Differential Privacy (Opacus), and FastAPI.

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