federated learning

A Data Management Mechanism for Federated Learning Models using a CID-based Reference and Verification Structure

Privacy-Enhancing Federated Learning Models for Cybersecurity in IoT Networks

Federated Learning is investigated as a decentralized approach to intrusion detection that enables local model training on IoT edge devices while transmitting only encrypted model updates to a central server, thereby preserving data privacy and reducing communication overhead.

Decentralized Federated Learning with Byzantine Fault Tolerance using Blockchain-Based Verification

Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets without directly sharing the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject poisoned data or manipulate model updates, ultimately com...

A machine learning framework for privacy preserving personalized multimodal emotion recognition

This paper presents a novel machine learning framework, Privacy-Preserving Personalized Multimodal Emotion Recognition (P3MER), that simultaneously addresses three fundamental challenges in affective computing: achieving state-of-the-art recognition accuracy, ensuring robust privacy protection, and enabling effective p...

The Decentralized Classroom: A Narrative Review of Federated Learning from Google's Keyboards to the Privacy's Frontier

Federated learning---the artificial intelligence whose subject is the decentralized classroom and whose lesson is the model's travel---moved from Dwork's 2006 differential privacy and Shokri and Shmatikov's 2015 gradients through Konečný's 2016 compression, McMahan's 2017 FedAvg, and Bonawitz's 2017 aggregation to Zhao...

Information-Theoretic Framework for Trustworthy Federated Learning

Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a rigorous theoretical basis. This work introduces an informat...

Decentralized Federated Learning with Differential Privacy for Scientific Data

This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent privacy regulations and intellectual property protections. Trad...

Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation

Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data fro...

Federated Learning with Differential Privacy: Enhancing Privacy in Decentralized Model Training

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, the inherent collaborative nature of FL still poses privacy risks. This paper investigates the integration of differential privacy (DP) into the FL framewor...

**Decentralized Federated Learning with Byzantine Fault Tolerance using Graph Protocols**

This record contains a research paper preprint and its associated PDF document.

Distributed Bayesian Inference with Federated Learning and Differential Privacy

This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a frame...

Decentralized Learning with Federated Graph Neural Networks

This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs...