Centralized Training

Centralized training, a machine learning paradigm, aims to leverage the benefits of centralized data processing for model training while maintaining decentralized execution to preserve data privacy and scalability. Current research focuses on addressing challenges like data heterogeneity and communication efficiency in federated learning settings, often employing techniques such as model aggregation with client clustering or employing diffusion models for data synthesis. This approach is significant for enabling collaborative model training across distributed datasets while mitigating privacy concerns, with applications ranging from medical image analysis and code generation to multi-agent reinforcement learning and network anomaly detection.

Papers

May 17, 2023

Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
Jiong Zhu, Aishwarya Reganti, Edward Huang, Charles Dickens, Nikhil Rao, Karthik Subbian, Danai Koutra
Neural Network Training Data Model Aggregation Centralized Training Training Graph GNN Training

May 6, 2023

Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model
Mingzhao Yang, Shangchao Su, Bin Li, Xiangyang Xue
Diffusion Model Semi Supervised Pre Trained Diffusion Model Centralized Training Co Training One Shot Federated Learning Client Data

April 28, 2023

From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL
Dapeng Li, Zhiwei Xu, Bin Zhang, Guoliang Fan
New Paradigm Centralized Training Learning Paradigm Decentralized Execution MARL Algorithm Cooperative Learning Explicit Communication Tacit Knowledge

March 13, 2023

Network Anomaly Detection Using Federated Learning
William Marfo, Deepak K. Tosh, Shirley V. Moore
Centralized Training Centralized Machine Learning Network Anomaly Detection Efficient Anomaly Detection

February 24, 2023

FLINT: A Platform for Federated Learning Integration
Ewen Wang, Ajay Kannan, Yuefeng Liang, Boyi Chen, Mosharaf Chowdhury
Diverse Platform Centralized Training Cross Device Federated Learning Cross Platform Device Cloud Collaborative

January 28, 2023

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
Pengyu Zhang, Yingbo Zhou, Ming Hu, Xian Wei, Mingsong Chen
Continual LEArning Federated Learning Centralized Training Cyclic Learning

December 23, 2022

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
Chanyoung Park, Haemin Lee, Won Joon Yun, Soyi Jung, Joongheon Kim
Multi Agent Deep Reinforcement Learning Multiple Agent Cooperative Multi Agent Reinforcement Learning Multiple Unmanned Aerial Vehicle Centralized Training UAV Swarm Autonomous Mobility on Demand

November 17, 2022

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Ahmad Chaddad, Qizong lu, Jiali Li, Yousef Katib, Reem Kateb, Camel Tanougast, Ahmed Bouridane, Ahmed Abdulkadir
Artificial Intelligence Data Driven Explainable Artificial Intelligence Centralized Training Federated Machine Learning

October 28, 2022

October 17, 2022

PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning
Yiqun Chen, Hangyu Mao, Jiaxin Mao, Shiguang Wu, Tianle Zhang, Bin Zhang, Wei Yang, Hongxing Chang
Multi Agent Reinforcement Learning Centralized Training Personalization Task

October 12, 2022

Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha, Pedro A. Santos, Ana Paiva, Francisco S. Melo
Multi Agent Reinforcement Learning Markov Decision Process Partial Observability Centralized Training Hybrid Framework

September 30, 2022

Federated Training of Dual Encoding Models on Small Non-IID Client Datasets
Raviteja Vemulapalli, Warren Richard Morningstar, Philip Andrew Mansfield, Hubert Eichner, Karan Singhal, Arash Afkanpour, Bradley Green
Representation Learning Loss Function Centralized Training Small Scale Federated Training Client Data

September 26, 2022

More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization
Jiangxing Wang, Deheng Ye, Zongqing Lu
Multi Agent Actor Critic Centralized Training Stochastic Policy Decentralized Execution Joint Policy

September 20, 2022

Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning
Yitian Hong, Yaochu Jin, Yang Tang
Cooperative Multi Agent Reinforcement Learning Centralized Training Agent Benchmark

August 4, 2022

Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents
Xuting Tang, Jia Xu, Shusen Wang
Multi Agent Reinforcement Learning Shot Learning Model Adaptation Centralized Training Transfer Reinforcement Learning Collaborative Agent

May 25, 2022

Scalable Multi-Agent Model-Based Reinforcement Learning
Vladimir Egorov, Aleksei Shpilman
Multi Agent Reinforcement Learning Model Based Reinforcement Learning Centralized Training

May 3, 2022

Training Mixed-Domain Translation Models via Federated Learning
Peyman Passban, Tanya Roosta, Rahul Gupta, Ankit Chadha, Clement Chung
Federated Learning Neural Machine Translation Centralized Training Mixed Domain

April 7, 2022

FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement
Hao Zhang, Tingting Wu, Siyao Cheng, Jie Liu
Data Heterogeneity Performance Improvement Global Model Centralized Training Federated Optimization Iterative Method

March 16, 2022

CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
Jian Zhao, Xunhan Hu, Mingyu Yang, Wengang Zhou, Jiangcheng Zhu, Houqiang Li
Multi Agent Reinforcement Learning Decentralized Learning Centralized Training

Centralized Training

Papers

Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation

Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model

From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL

Network Anomaly Detection Using Federated Learning

FLINT: A Platform for Federated Learning Integration

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Federated Learning based Energy Demand Prediction with Clustered Aggregation

Federated Learning with Intermediate Representation Regularization

PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning

Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning

Federated Training of Dual Encoding Models on Small Non-IID Client Datasets

More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization

Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning

Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents

Scalable Multi-Agent Model-Based Reinforcement Learning

Training Mixed-Domain Translation Models via Federated Learning

FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement

CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning