federated learning

ARTIFICIAL INTELLIGENCE IN FINANCIAL STATEMENT FRAUD DETECTION: A CRITICAL SYNTHESIS OF EMERGING TRENDS, METHODOLOGIES, AND FUTURE DIRECTIONS FOR U.S. PUBLICLY TRADED COMPANIES

Financial statement fraud causes systemic damage to U.S. capital markets, investor confidence, and the credibility of public company disclosures. Artificial intelligence (AI) offers promising new detection capabilities, yet its evidence base and operational readiness remain unevenly developed across the literature. Thi...

FFT-GA-BC: a fused federated transformer genetic algorithm framework with blockchain-secured, differentially private, homomorphically encrypted aggregation for mutation, phenotype, and binary-allele analysis

Federated learning enables cross-institutional clinical-genetics analytics without sharing raw patient data; yet, no existing framework integrates the full cryptographic stack–differential privacy, homomorphic encryption, and blockchain auditing—required for regulatory deployment. Here, we present FFT-GA-BC, a secure-b...

A Study on Trustworthy Aggregation Mechanisms for Blockchain-Based Federated Learning

To investigate how blockchain technology can improve the reliability, speed and legality of federated learning, this paper concentrates on the process of model combination in federated learning. By dealing with the problems of single vulnerable points and insufficient trust in centralized combination, the paper compreh...

CONCRETE, SCIENTIFIC AND INTEGRATIVE EDUCATION

CONCRETE, SCIENTIFIC AND INTEGRATIVE EDUCATIONA Proposal for Reorganizing Learning Based on Reality, Investigation, Demonstration, Science, Territory, and the Integration of Knowledge Author: Cláudio Vicente da SilvaLocation: Londrina, Paraná, BrazilDate: October 8, 2026 DESCRIPTION This work presents a proposal for th...

A federated learning framework for Non-IID data : data feature optimization and personalization strategy

medchain: reputation-driven committee-consensus federated learning for trustworthy healthcare blockchains

medchain v0.1.35 (2026-10-08) First public release. medchain implements reputation-driven, committee-consensus federated learning for trustworthy healthcare blockchains. This release finalises the v0.1.34 freeze candidate by completing the author and repository metadata and adding the release date required for archivin...

Inclusive Learning in Decentralized Metaverse Ecosystems

This chapter discusses ways decentralized metaverse classrooms promote inclusive, collaborative and learner-centered education at the same time bringing out challenges of accessibility, privacy, governance and equity. Based on contemporary literature on immersive learning, digital transformation and decentralized educa...

Systemic privacy risks of personal data exposure through conversational large language model agents

Abstract Conversational Large Language Model (LLM) agents, deployed quickly, have exposed unprecedented opportunities for personal information, from health data to financial information, to biometric identifiers. This critical enlightenment review investigates general privacy dangers in each of five areas: data leakage...

Early Diagnosis of Chronic Diseases Using AI-Enabled Wearable Devices

Chronic diseases such as cardiovascular disorders, diabetes, respiratory illnesses, neurological conditions, and sleep disorders require early diagnosis to improve patient outcomes and reduce healthcare costs. Recent advances in Artificial Intelligence (AI), wearable sensors, and the Internet of Medical Things (IoMT) h...

Intelligent Wearable Systems for Continuous Healthcare Monitoring and Personalized Medicine

Smart textiles integrated with advanced biosignal processing technologies are transforming healthcare by enabling continuous, real-time, and non-invasive monitoring through wearable systems. These wearable platforms continuously acquire and analyze physiological signals such as ECG, EMG, EEG, respiration, body temperat...

Less is More: Variance-Gated Client Rejection for Faster Convergence in Federated Learning

This paper proposes VGCR-FL, a method that speeds up federated learning by filtering out client updates that deviate strongly from the rest of the cohort. The filter uses a threshold that adapts each round, plus a check on whether the update points in the same direction as the cohort. This keeps useful updates from min...

THE EL-RAKHAWI DOCTRINE OF SELF-GOVERNING UBIQUITOUS SYSTEMS From Technical Infrastructure to Comprehensive Legal Governance

The El-Rakhawi Doctrine of Self-Governing Ubiquitous Systems presents a unified normative-theoretical framework addressing the critical gap between rapidly evolving autonomous computing technologies and inadequate legal governance structures. As ubiquitous systems transition from passive instruments to self-governing a...