federated learning

A Systematic Literature Review of Emerging Big Data Analytics Using Hadoop: Applications, Trends, and Implications for Sustainability

The rapid expansion of Big Data has transformed data-driven decision-making across diverse sectors, including healthcare, cybersecurity, smart cities, and the Internet of Things (IoT). However, traditional data management platforms struggle to address the increasing volume, velocity, variety, and veracity of modern dat...

Spatiotemporal vision transformers with Byzantine-robust federated prompt tuning for continuous urban perception

While spatial Vision Transformers (ViTs) achieve high precision in urban scene parsing, their frame-by-frame application in autonomous driving suffers from severe temporal flickering and prohibitive retraining costs across decentralized vehicle fleets. To overcome these dual bottlenecks, this paper introduce...

基于自适应参数调整的联邦学习 (Adaptive Parameter Adjustment Federated Learning)

Federated Learning (FL) is a machine learning technique that enables training models on decentralized data sources while preserving data privacy. However, traditional FL approaches often rely on global model updates, which can be vulnerable to adversarial attacks and suboptimal performance on heterogeneous data. This p...

Blockchain-Based Federated Learning with Differential Privacy

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL systems remain vulnerable to privacy breaches and data manipulation. This paper proposes a novel blockchain-based architecture that addresses these c...

Hybrid AI-Blockchain Frameworks for Secure Spectrum Data Governance in Smart Grids

The emerging 6G technologies will bring billions of IoT sensors which are going to demand little radio spectrum. The resulting high level of connectivity poses some novel problems, among them network congestion, and privacy: because energy-consumption data might be shared unintentionally, it provides insight into perso...

Federated Learning security with FedEDAuth: Embedding-Distribution Authentication for Counterfeit IC Detection

A Device-Agnostic Framework for Personalized Anomaly Detection in Wearable and Mobile Ecosystems: Applications in Personal Safety and Respiratory Health Surveillance

Abstract—This paper presents a reformulated, device-agnostic architectural framework for two interrelated applications: (i) personalized audio-based personal safety threat detection, and (ii) respiratory pattern anomaly detection for early-stage epidemiological screening. Originating from an ideation dialogue, the prop...

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

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

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...

Adaptive federated edge intelligence with semantic communication and trust-aware optimization for heterogeneous IoT networks

The rapid growth of heterogeneous Internet of Things (IoT) networks introduces significant challenges in communication efficiency, energy consumption, and reliable distributed intelligence due to diverse device capabilities, dynamic channel conditions, and limited computational resources. Existing bit-level communicati...

Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection

Abstract While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels. Federated noisy label learni...

Training Without Gathering the Data: A Historical Development 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...