Cover Image

Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Aketi, Sai Aparna, Choudhary, Sakshi, Roy, Kaushik
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

SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Data
by: Choudhary, Sakshi, et al.
Published: (2024)

Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning
by: Ravikumar, Deepak, et al.
Published: (2023)

Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
by: Ye, Haoxiang, et al.
Published: (2025)

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

DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
by: Wang, Zhiyu, et al.
Published: (2026)

The Built-In Robustness of Decentralized Federated Averaging to Bad Data
by: Sabella, Samuele, et al.
Published: (2025)

On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
by: Wang, Leo Muxing, et al.
Published: (2024)

Decentralized Learning Made Easy with DecentralizePy
by: Dhasade, Akash, et al.
Published: (2023)

Dynamic Topology Optimization for Non-IID Data in Decentralized Learning
by: Cox, Bart, et al.
Published: (2026)

Fair Decentralized Learning
by: Biswas, Sayan, et al.
Published: (2024)

Osmotic Learning: A Self-Supervised Paradigm for Decentralized Contextual Data Representation
by: Colosi, Mario, et al.
Published: (2025)

Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC
by: Wei, Xinming, et al.
Published: (2025)

Convergence Analysis of Split Federated Learning on Heterogeneous Data
by: Han, Pengchao, et al.
Published: (2024)

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2025)

FLASH: Federated Learning Across Simultaneous Heterogeneities
by: Chang, Xiangyu, et al.
Published: (2024)

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
by: Li, Zilinghan, et al.
Published: (2023)

Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2024)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
by: Chen, Yiyue, et al.
Published: (2025)

TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by: Hu, Gangqiang, et al.
Published: (2024)

Low-Cost Privacy-Preserving Decentralized Learning
by: Biswas, Sayan, et al.
Published: (2024)

Get More for Less in Decentralized Learning Systems
by: Dhasade, Akash, et al.
Published: (2023)

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
by: Wang, Haoming, et al.
Published: (2023)

FineServe: Precision-Aware KV Slab and Two-Level Scheduling for Heterogeneous Precision LLM Serving
by: Bin, Kyungmin, et al.
Published: (2025)

Adaptive Execution Scheduler for DataDios SmartDiff
by: Poduri, Aryan
Published: (2025)

Fused3S: Fast Sparse Attention on Tensor Cores
by: Li, Zitong, et al.
Published: (2025)

HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
by: Kim, Gyudong, et al.
Published: (2024)

Robust Decentralized Learning with Local Updates and Gradient Tracking
by: Ghiasvand, Sajjad, et al.
Published: (2024)

Energy-Aware Decentralized Learning with Intermittent Model Training
by: Dhasade, Akash, et al.
Published: (2024)

Protocol Learning, Decentralized Frontier Risk and the No-Off Problem
by: Long, Alexander
Published: (2024)

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

Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature
by: Yang, Ruikai, et al.
Published: (2024)

Learning to Schedule Online Tasks with Bandit Feedback
by: Xu, Yongxin, et al.
Published: (2024)

Scheduling for On-Board Federated Learning with Satellite Clusters
by: Razmi, Nasrin, et al.
Published: (2024)

Aryl: An Elastic Cluster Scheduler for Deep Learning
by: Li, Jiamin, et al.
Published: (2022)

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

Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
by: Sen, Mrinmay, et al.
Published: (2025)

SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data
by: Yang, Mingkun, et al.
Published: (2025)

Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
by: Yang, Qiantao, et al.
Published: (2026)

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

Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
by: Xiong, Guojun, et al.
Published: (2023)