federated learning

Training Without Gathering the Data: A Critical Survey Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...

Decentralized Verification of Federated Learning Models

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without direct data sharing. However, ensuring the integrity and accuracy of these models remains a significant challenge. Traditional verification methods often rely on centralized aggregation...

Federated learning for renewable energy systems: Algorithms, applications, and deployment challenges

zk-DEAP-artifact-1.0

Secure aggregation requires operating on private inputs while preserving confidentiality, verifying that each input satisfies constraints, and avoiding trusted parties. This scheme appears in federated learning, as well as Internet of Things, operational technology, blockchain, smart-grid, and voting deployments. Exist...

Decentralized Federated Learning with Byzantine Fault Tolerance via Proof-of-Stake

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants intentionally submit incorrect or misleading updates, comprom...

FedVigil: Defending backdoor attacks in federated learning via dynamic Wasserstein-HDBSCAN vigilance

Differential Privacy for Federated Learning with Adaptive Noise

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, the inherent privacy risks associated with aggregating model updates introduce significant challenges. Traditional differential privacy (DP) tech...

Telemetry Dataset for Federated Learning on Heterogeneous Edge Devices Using Raspberry Pi 5 and NVIDIA Jetson Nano

This dataset accompanies the manuscript "Telemetry-aware federated learning on heterogeneous edge devices: an experimental study". The dataset contains telemetry measurements collected from a federated learning testbed consisting of a Raspberry Pi 5 and an NVIDIA Jetson Nano running the Flower federated learning framew...

Decentralized Machine Learning via Federated Learning with Byzantine Fault Tolerance

Federated learning (FL) offers a promising approach to distributed machine learning, enabling model training on decentralized devices without directly exchanging sensitive data. However, the inherent vulnerability of FL systems to Byzantine failures – where malicious participants intentionally corrupt the learning proc...

Telemetry Dataset for Federated Learning on Heterogeneous Edge Devices Using Raspberry Pi 5 and NVIDIA Jetson Nano

This dataset accompanies the manuscript "Telemetry-aware federated learning on heterogeneous edge devices: an experimental study". The dataset contains telemetry measurements collected from a federated learning testbed consisting of a Raspberry Pi 5 and an NVIDIA Jetson Nano running the Flower federated learning framew...

Replication package for Flows and Rounds: benchmark and retrieval scripts for distributed neurodynamic optimization and federated learning

This package contains the code and generated data behind the quantitative results of the survey "Flows and Rounds: A Survey of Distributed Neurodynamic Optimization and Federated Learning", submitted to Neurocomputing. It reproduces every number, table, and figure in Sections 2.3 and 8 of the article. No third-party, p...

Decentralized Machine Learning via Federated Learning with Byzantine Fault Tolerance

Federated learning (FL) offers a promising approach to distributed machine learning, enabling model training on decentralized devices without directly exchanging sensitive data. However, the inherent vulnerability of FL systems to Byzantine failures – where malicious participants intentionally corrupt the learning proc...