Buchumschlag

MAPL: Model Agnostic Peer-to-peer Learning

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Mukherjee, Sayak, Simonetto, Andrea, Jamali-Rad, Hadi
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning Artificial Intelligence Cryptography and Security Distributed, Parallel, and Cluster Computing
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning
von: Xu, Zhangchen, et al.
Veröffentlicht: (2024)

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
von: Lee, Younghan, et al.
Veröffentlicht: (2024)

Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation
von: Alsulaimawi, Zahir
Veröffentlicht: (2024)

Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)
von: Ebron, Sheldon C., et al.
Veröffentlicht: (2023)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
von: Cyffers, Edwige, et al.
Veröffentlicht: (2022)

QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning
von: Mia, Md Jueal, et al.
Veröffentlicht: (2024)

Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
von: Ozfatura, Kerem, et al.
Veröffentlicht: (2022)

Noiseless Privacy-Preserving Decentralized Learning
von: Biswas, Sayan, et al.
Veröffentlicht: (2024)

Protecting Split Learning by Potential Energy Loss
von: Zheng, Fei, et al.
Veröffentlicht: (2022)

Vertical Federated Learning: Concepts, Advances and Challenges
von: Liu, Yang, et al.
Veröffentlicht: (2022)

Private Knowledge Sharing in Distributed Learning: A Survey
von: Supeksala, Yasas, et al.
Veröffentlicht: (2024)

Inclusive, Differentially Private Federated Learning for Clinical Data
von: Parampottupadam, Santhosh, et al.
Veröffentlicht: (2025)

BAFFLE: A Baseline of Backpropagation-Free Federated Learning
von: Feng, Haozhe, et al.
Veröffentlicht: (2023)

Private Model Personalization Revisited
von: Snedeker, Conor, et al.
Veröffentlicht: (2025)

Federated Learning for Cyber Physical Systems: A Comprehensive Survey
von: Quan, Minh K., et al.
Veröffentlicht: (2025)

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
von: Zuo, Xuhan, et al.
Veröffentlicht: (2024)

Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
von: Zhang, Meiying, et al.
Veröffentlicht: (2024)

POPri: Private Federated Learning using Preference-Optimized Synthetic Data
von: Hou, Charlie, et al.
Veröffentlicht: (2025)

Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
von: Akhtarshenas, Azim, et al.
Veröffentlicht: (2023)

DSperse: A Framework for Targeted Verification in Zero-Knowledge Machine Learning
von: Ivanov, Dan, et al.
Veröffentlicht: (2025)

StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
von: Bouzinis, Pavlos S., et al.
Veröffentlicht: (2024)

Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC
von: Lin, Ke, et al.
Veröffentlicht: (2024)

Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning
von: Xie, Tianxiu, et al.
Veröffentlicht: (2024)

FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems
von: Turazza, Fabio, et al.
Veröffentlicht: (2026)

On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation
von: Alhaidari, Abdulrahman, et al.
Veröffentlicht: (2025)

Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise
von: Rodio, Angelo, et al.
Veröffentlicht: (2025)

Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
von: Zhang, Jianing, et al.
Veröffentlicht: (2025)

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
von: Tyagi, Sahil, et al.
Veröffentlicht: (2025)

FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
von: Liu, Sheng, et al.
Veröffentlicht: (2026)

Quantum-Enhanced Generative Models for Rare Event Prediction
von: Haider, M. Z., et al.
Veröffentlicht: (2025)

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
von: Liu, Ji, et al.
Veröffentlicht: (2024)

Exploring AI-Enabled Cybersecurity Frameworks: Deep-Learning Techniques, GPU Support, and Future Enhancements
von: Becher, Tobias, et al.
Veröffentlicht: (2024)

Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
von: Puppala, Sai, et al.
Veröffentlicht: (2024)

FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol
von: Nikolakakis, Konstantinos E., et al.
Veröffentlicht: (2024)

D2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative Learning
von: Srivastava, Yash, et al.
Veröffentlicht: (2025)

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix
von: Wu, Di, et al.
Veröffentlicht: (2025)

Blockchain for Large Language Model Security and Safety: A Holistic Survey
von: Geren, Caleb, et al.
Veröffentlicht: (2024)

MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
von: Wang, Tiantong, et al.
Veröffentlicht: (2026)

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
von: Mehmood, Haaris, et al.
Veröffentlicht: (2026)

UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
von: Lai, Shih-Yu, et al.
Veröffentlicht: (2026)