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federated learning

2,117 papers

#federated learning Open access Sep 2026

MAQDRL: QuadTree-Based Multi-Agent Federated Deep Q-Learning for Collision Avoidance and Routing Optimization with MEC-Aware Communication Analysis

Experimental results demonstrate that the proposed reinforcement learning-based Multi-agent Federated Deep Q-Network model, which integrates federated learning with QuadTree spatial partitioning and indexing framework for collision detection, is a promising solution for advancing autonomous vehicle routing systems.

A. Raj, Anurag Sharma, K. Naik · 0 citations
#federated learning Open access Sep 2026

Decentralized Federated Optimization with Byzantine Fault Tolerance via Blockchain

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of FL to malicious participants, known as Byzantine attacks, poses a significant threat to the integrity and convergence of the lear...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Proof-Carrying Cryptography for Federated Learning

Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets while preserving data privacy. However, current FL security mechanisms often rely on centralized trust assumptions, posing significant vulnerabilities. This paper proposes a novel security framework leverag...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Differential Privacy for Federated Learning via Homomorphic Encryption

This paper presents a novel approach to enhancing data privacy in federated learning by integrating differential privacy with homomorphic encryption. Federated learning, a distributed machine learning paradigm, allows training models across multiple devices without directly sharing raw data. However, this approach stil...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Distributed Differential Privacy with Federated Learning using Byzantine Fault Tolerance

This paper presents a novel approach to achieving robust differential privacy within federated learning systems, specifically designed to mitigate the risks posed by Byzantine attacks. Traditional federated learning methods frequently struggle to provide rigorous privacy guarantees when confronted with malicious partic...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Quantum Walk Based Federated Learning

Federated learning (FL) offers a promising approach to distributed machine learning by enabling collaborative model training without directly exchanging raw data. However, traditional FL systems are plagued by privacy vulnerabilities and significant communication costs. This paper proposes a novel framework, Quantum Wa...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Decentralized Machine Learning for Autonomous Vehicle Control

This paper investigates the application of decentralized machine learning (DML) to autonomous vehicle control. Traditional centralized approaches to autonomous driving often face challenges related to scalability, communication bottlenecks, and vulnerability to single points of failure. This research proposes a novel s...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Differential Privacy with Federated Learning via Entangled Noise

Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized datasets without directly exchanging the data itself. However, integrating differential privacy (DP) into FL introduces significant accuracy trade-offs. This work proposes a novel approach, "Entangled Noise," to mi...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Privacy-Preserving Federated Learning for Computational Pharmaceutical Applied Aspect in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a pathway toward privacy-preserving computational pharmaceutical analysis across Ugandan health facilities, yet its practical utility is constrained by two under-examined statistical realities: the non-independence of client data distributions and the scarcity of validated labels. We introduce...

Achieng Maureen Adong, Kizza Ivan Mugisha, Nakimuli Sarah Wamala · 0 citations
#federated learning Open access Sep 2026

Based on Differential Privacy Federated Learning Optimization Algorithm

This paper introduces a novel federated learning optimization algorithm that integrates differential privacy mechanisms to safeguard user data privacy while simultaneously enhancing learning efficiency. Federated learning, while promising for distributed model training, presents significant privacy challenges. Our prop...

Jincheng Zhang · 0 citations
#federated learning Open access Sep 2026

Privacy-Preserving Federated Learning for Computational Pharmaceutical Applied Aspect in Uganda: Robustness Under Distribution Shift and Sparse Labels

Federated learning offers a pathway toward privacy-preserving computational pharmaceutical analysis across Ugandan health facilities, yet its practical utility is constrained by two under-examined statistical realities: the non-independence of client data distributions and the scarcity of validated labels. We introduce...

Achieng Maureen Adong, Kizza Ivan Mugisha, Nakimuli Sarah Wamala · 0 citations
#federated learning Open access Sep 2026

Federated Learning with Blockchain-Based Security Mechanisms

This paper proposes a novel framework for secure federated learning leveraging the inherent properties of blockchain technology. The core claim is that utilizing blockchain's distributed consensus mechanism can establish a robust and reliable system for federated learning, effectively safeguarding the privacy of partic...

Jincheng Zhang · 0 citations

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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