Heterogeneous Data

Heterogeneous data, encompassing datasets with varying distributions, formats, and qualities across sources, presents a significant challenge in machine learning. Current research focuses on developing robust algorithms and model architectures, such as federated learning with adaptive aggregation and personalized models, to effectively handle this heterogeneity in diverse applications like medical imaging and industrial settings. These efforts aim to improve model accuracy, fairness, and robustness while addressing privacy concerns inherent in decentralized data collection. The successful management of heterogeneous data is crucial for advancing machine learning's applicability to real-world problems where data is inherently diverse and distributed.

Papers

August 1, 2024

Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures
Alexandre Trilla, Nenad Mijatovic
Causal Discovery Mutual Information Stronger Generalizability Heterogeneous Data Underlying Causal Automatic Discovery Bivariate Observational Data

July 25, 2024

Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets
Madapu Amarlingam, Abhishek Wani, Adarsh NL
Federated Learning Heterogeneous Data Client Model Personalized Fl Asset Class

July 23, 2024

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
Shengkun Zhu, Jinshan Zeng, Sheng Wang, Yuan Sun, Xiaodong Li, Yuan Yao, Zhiyong Peng
Native Robustness Procedural Fairness Heterogeneous Data Heterogeneous Federated Learning Alternating Direction Method of Multiplier Statistical Heterogeneity Superlinear Convergence Personalized Fl

July 7, 2024

Learning to Price Homogeneous Data
Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy
LeArning Abstract Heterogeneous Data O$ Regret Online Learning Problem Data Pricing

July 4, 2024

FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning
Sujit Chowdhury, Raju Halder
Data Heterogeneity Heterogeneous Data Client Model Cost Sensitive Mean Aggregation

June 14, 2024

Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters
Yuhang Zhou, Zihua Zhao, Haolin Li, Siyuan Du, Jiangchao Yao, Ya Zhang, Yanfeng Wang
Training Data Mixture Component Heterogeneous Data Low Rank Adapter Heterogeneous Training

June 12, 2024

May 31, 2024

Non-Federated Multi-Task Split Learning for Heterogeneous Sources
Yilin Zheng, Atilla Eryilmaz
Data Heterogeneity Heterogeneous Data Split Learning Heterogeneous Computing Heterogeneous Source Federated Split Learning

May 27, 2024

Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data
Xiaolu Wang, Yuchang Sun, Hoi-To Wai, Jun Zhang
Heterogeneous Data Asynchronous Training Asynchronous Stochastic Gradient Descent

May 22, 2024

SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Data
Sakshi Choudhary, Sai Aparna Aketi, Kaushik Roy
Deep Learning Algorithm Heterogeneous Data Sharpness Aware Minimization Decentralized Training Communication Compression Decentralized Deep Learning Saddle to Saddle Dynamic

May 21, 2024

May 2, 2024

Sharp Bounds for Sequential Federated Learning on Heterogeneous Data
Yipeng Li, Xinchen Lyu
Federated Learning Heterogeneous Data Convergence Guarantee Convex Function Non Convex Objective Convex Objective Sharp Bound

April 25, 2024

GuideWalk: A Novel Graph-Based Word Embedding for Enhanced Text Classification
Sarmad N. Mohammed, Semra Gündüç
Machine Learning Text Classification Heterogeneous Data Text Data Data Fusion Seated Walk Enhanced Learning Multi Label Document Classification

March 25, 2024

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
Yuxin Zhang, Haoyu Chen, Zheng Lin, Zhe Chen, Jin Zhao
Heterogeneous Data Layer Wise Clustered Federated Learning

March 21, 2024

Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server
Fei Li, Chu Kiong Loo, Wei Shiung Liew, Xiaofeng Liu
Multi Task Learning Heterogeneous Data Efficient Approach FedProx Algorithm Feature Name Simultaneous Improvement

March 5, 2024

Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data
Sai Aparna Aketi, Sakshi Choudhary, Kaushik Roy
Decentralized Learning Heterogeneous Data Data Distribution Unobserved Heterogeneity Variable Rate

March 3, 2024

Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity
Seyed Mohammad Azimi-Abarghouyi, Viktoria Fodor
Heterogeneous Data Model Aggregation Robust Framework Hierarchical Federated Learning Statistical Heterogeneity Aggregated Gradient

February 26, 2024

Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models
Jinqian Chen, Jihua Zhu, Qinghai Zheng, Zhongyu Li, Zhiqiang Tian
Heterogeneous Data Human Head Federated Setting Ensemble Diversity Reliable Prediction Projection Head

Heterogeneous Data

Papers

Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures

Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

Learning to Price Homogeneous Data

FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning

Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters

Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data

DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels

Non-Federated Multi-Task Split Learning for Heterogeneous Sources

Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data

SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Data

Efficient and Interpretable Information Retrieval for Product Question Answering with Heterogeneous Data

Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory

Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

GuideWalk: A Novel Graph-Based Word Embedding for Enhanced Text Classification

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data

Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server

Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data

Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity

Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models