Cover Image

OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Masmoudi, Nizar, Jaafar, Wael
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning
by: Guo, Shunxin, et al.
Published: (2025)

S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
by: Sánchez, Pedro Miguel Sánchez, et al.
Published: (2025)

Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
by: de Souza, Allan M., et al.
Published: (2024)

Greedy Shapley Client Selection for Communication-Efficient Federated Learning
by: Singhal, Pranava, et al.
Published: (2023)

Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
by: Yuan, Liangqi, et al.
Published: (2024)

SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
by: Kim, Minsu, et al.
Published: (2024)

GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
by: Zhang, Tuo, et al.
Published: (2023)

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
by: Tian, Chunlin, et al.
Published: (2024)

DIGEST: Fast and Communication Efficient Decentralized Learning with Local Updates
by: Gholami, Peyman, et al.
Published: (2023)

AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
by: Jia, Chentao, et al.
Published: (2023)

FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning
by: Stricker, F., et al.
Published: (2025)

TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
by: Wang, Mengdi, et al.
Published: (2024)

CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
by: Xia, Zeke, et al.
Published: (2024)

PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale
by: Luqman, Alka, et al.
Published: (2024)

BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework
by: Qin, Zhen, et al.
Published: (2022)

Decentralized Federated Learning with Model Caching on Mobile Agents
by: Wang, Xiaoyu, et al.
Published: (2024)

Masked Random Noise for Communication Efficient Federated Learning
by: Li, Shiwei, et al.
Published: (2024)

Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
by: Balivada, Nihal, et al.
Published: (2026)

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data
by: Marisetty, Harsha Varun, et al.
Published: (2025)

FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
by: Ning, Zhiyuan, et al.
Published: (2024)

Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning
by: Guo, Shunxin, et al.
Published: (2025)

Communication and Computation Efficient Split Federated Learning in O-RAN
by: Gu, Shunxian, et al.
Published: (2025)

Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
by: Zhang, Zihan, et al.
Published: (2025)

Communication-Efficient Federated Optimization over Semi-Decentralized Networks
by: Wang, He, et al.
Published: (2023)

Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
by: Zehtabi, Shahryar, et al.
Published: (2024)

Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
by: Feng, Chao, et al.
Published: (2025)

GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning
by: Na, Shijie, et al.
Published: (2024)

Decentralized Federated Learning Over Imperfect Communication Channels
by: Li, Weicai, et al.
Published: (2024)

Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
by: Pan, Qiying, et al.
Published: (2023)

Communication Efficient and Provable Federated Unlearning
by: Tao, Youming, et al.
Published: (2024)

FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
by: Li, Shiwei, et al.
Published: (2024)

Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning
by: Tang, Zichen, et al.
Published: (2024)

Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
by: Theologitis, Michail, et al.
Published: (2024)

Communication-Efficient Federated Fine-Tuning
by: Theologitis, Michael, et al.
Published: (2025)

Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
by: Perazzone, Jake B., et al.
Published: (2025)

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
by: Li, Shiwei, et al.
Published: (2025)

AdFL: In-Browser Federated Learning for Online Advertisement
by: Alemari, Ahmad, et al.
Published: (2026)

AugFL: Augmenting Federated Learning with Pretrained Models
by: Yue, Sheng, et al.
Published: (2025)

Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
by: Marfo, William, et al.
Published: (2025)

Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
by: Rangwala, Murtaza, et al.
Published: (2025)