Image de couverture de livre

Power to the Clients: Federated Learning in a Dictatorship Setting

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Alipour, Mohammadsajad, Amiri, Mohammad Mohammadi
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence Computation and Language Cryptography and Security Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
par: Herath, Kavindu, et autres
Publié: (2026)

Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning
par: Zhang, Meiying, et autres
Publié: (2024)

Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
par: Li, Qilei, et autres
Publié: (2024)

HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
par: Quan, Minh K., et autres
Publié: (2024)

A Framework for Double-Blind Federated Adaptation of Foundation Models
par: Tastan, Nurbek, et autres
Publié: (2025)

Anomalous Client Detection in Federated Learning
par: Thakur, Dipanwita, et autres
Publié: (2024)

Toward Malicious Clients Detection in Federated Learning
par: Dou, Zhihao, et autres
Publié: (2025)

PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
par: Hou, Charlie, et autres
Publié: (2024)

Federated Learning Client Pruning for Noisy Labels
par: Morafah, Mahdi, et autres
Publié: (2024)

Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
par: Akhtarshenas, Azim, et autres
Publié: (2023)

Random Client Selection on Contrastive Federated Learning for Tabular Data
par: Ginanjar, Achmad, et autres
Publié: (2025)

Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
par: Oh, Seungeun, et autres
Publié: (2024)

Towards Reversible Model Merging For Low-rank Weights
par: Alipour, Mohammadsajad, et autres
Publié: (2025)

Vertical Federated Learning: Concepts, Advances and Challenges
par: Liu, Yang, et autres
Publié: (2022)

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
par: Younesi, Abolfazl, et autres
Publié: (2025)

CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning
par: DiMaggio, Anthony, et autres
Publié: (2025)

Inclusive, Differentially Private Federated Learning for Clinical Data
par: Parampottupadam, Santhosh, et autres
Publié: (2025)

BAFFLE: A Baseline of Backpropagation-Free Federated Learning
par: Feng, Haozhe, et autres
Publié: (2023)

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
par: Xu, Yichang, et autres
Publié: (2024)

Federated Learning for Cyber Physical Systems: A Comprehensive Survey
par: Quan, Minh K., et autres
Publié: (2025)

Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
par: Ozfatura, Kerem, et autres
Publié: (2022)

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
par: Zuo, Xuhan, et autres
Publié: (2024)

POPri: Private Federated Learning using Preference-Optimized Synthetic Data
par: Hou, Charlie, et autres
Publié: (2025)

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
par: Lee, Younghan, et autres
Publié: (2024)

StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
par: Bouzinis, Pavlos S., et autres
Publié: (2024)

Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
par: Zhang, Jianing, et autres
Publié: (2025)

Blockchain and Biometrics: Survey, GDPR Analysis, and Future Directions
par: Ghafourian, Mahdi, et autres
Publié: (2023)

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
par: Tyagi, Sahil, et autres
Publié: (2025)

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

FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
par: Liu, Sheng, et autres
Publié: (2026)

Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
par: Liu, Ji, et autres
Publié: (2024)

Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
par: Puppala, Sai, et autres
Publié: (2024)

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix
par: Wu, Di, et autres
Publié: (2025)

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

When Speculation Spills Secrets: Side Channels via Speculative Decoding In LLMs
par: Wei, Jiankun, et autres
Publié: (2024)

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

Federated Graph Condensation with Information Bottleneck Principles
par: Yan, Bo, et autres
Publié: (2024)

Federated Unlearning with Gradient Descent and Conflict Mitigation
par: Pan, Zibin, et autres
Publié: (2024)

Federated PCA on Grassmann Manifold for IoT Anomaly Detection
par: Nguyen, Tung-Anh, et autres
Publié: (2024)

RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
par: Penalva, Isaac Marroqui, et autres
Publié: (2025)