federated learning

Secure Federated Learning Framework with Dynamic Lightweight Cipher (Fed- DLC) for Resource-Constrained Networks and Data-Sensitive Applications

Advancements in deep learning for early breast cancer detection using artificial intelligence models in low resource settings

The most important contributions from this review are a quantitative comparison of efficiency for edge versus cloud deployment, detection of dataset bias, and practical recommendations regarding infrastructure, regulatory pathways, and privacy-preserving federated learning.

Diabetes Diagnosis: Comparison of Federated Learning Algorithms

FTCL: Federated Transfer Continual Learning with Outlier-Aware Adaptive EWC v2 and Episodic Replay for Personalized sEMG Gesture Recognition

Information Bottleneck with Differential Privacy for Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, thus addressing privacy concerns. However, FL is still susceptible to privacy breaches and suffers from significant information loss during model aggregation, a phenomenon a...

Decentralized Federated Learning with Byzantine Fault Tolerance via Consensus-Based Proofs

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 can inject faulty model updates, compromising the global mode...

Distributed Federated Learning with Differential Privacy and Byzantine Fault Tolerance

Federated Learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, existing FL frameworks are susceptible to various vulnerabilities, including privacy breaches through information leakage and attacks from Byzantine clients a...

Federated discretization for supervised classification

Abstract Several classifiers operate exclusively on discrete variables, requiring continuous attributes to be preprocessed through discretization. Supervised discretization addresses this need by partitioning the domain of continuous variables into intervals that define a discretization policy. Federated learning, in t...

Distributed Graph Learning via Federated Bayesian Networks

This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks...

Decentralized Federated Learning with Differential Privacy via Homomorphic Encryption

This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homomorphic encryption, allowing a central server to perform comp...

Decentralized Federated Learning with Byzantine Fault Tolerance via Consensus-Based Proofs

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 can inject faulty model updates, compromising the global mode...

A Federated Deep Learning Framework for Precise IoT Intrusion Detection and Reinforcement-based Automated Cyber Response

The rapid expansion of IoT deployments has created an attack surface that conventional intrusion detection systems are ill-equipped to defend.Centralized deep learning approaches compromise data privacy, while existing federated methods stop detection without addressing what the network should do next.This paper propos...