Cross Device Federated Learning

Cross-device federated learning (FL) aims to train machine learning models collaboratively across numerous mobile devices without directly sharing their private data. Current research emphasizes improving efficiency and robustness by addressing challenges like asynchronous updates, heterogeneous device capabilities, and imbalanced data distributions, often employing techniques such as secure aggregation, adaptive partial training, and novel optimization algorithms like stochastic proximal point methods. This field is significant for enabling privacy-preserving large-scale model training in diverse applications, particularly in mobile health and personalized services, while also pushing the boundaries of distributed systems and optimization theory.

Papers

October 17, 2024

On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts
Mustofa Ahmed, Abdul Muntakim, Nawrin Tabassum, Mohammad Asifur Rahim, Faisal Muhammad Shah
Deep Learning Model Mental Health Depression Detection Depression Symptom Smartphone Device Cross Device Federated Learning Reddit Post Federated Foundation Model

October 2, 2024

Debiasing Federated Learning with Correlated Client Participation
Zhenyu Sun, Ziyang Zhang, Zheng Xu, Gauri Joshi, Pranay Sharma, Ermin Wei
Federated Averaging Cross Device Federated Learning Optimal Separation Client Participation

June 5, 2024

Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning
Kun Wang, Yi-Rui Yang, Wu-Jun Li
Secure Aggregation Cross Device Federated Learning Asynchronous Aggregation

June 3, 2024

Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning
Kai Yi, Timur Kharisov, Igor Sokolov, Peter Richtárik
Training Data Client Selection FedAvg Converges Cross Device Federated Learning Expert Delineated Cohort Communication Round

May 30, 2024

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning
Avetik Karagulyan, Egor Shulgin, Abdurakhmon Sadiev, Peter Richtárik
LeArning Abstract Convex Loss Cross Device Federated Learning Stochastic Proximal Point Data Similarity Cross Device

April 15, 2024

Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su
Gossip Algorithm Cross Device Federated Learning

February 27, 2024

FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
Ziyue Xu, Mingfeng Xu, Tianchi Liao, Zibin Zheng, Chuan Chen
Global Model Local Model Large Scale Model Cross Device Federated Learning Effective Solution

February 16, 2024

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
Sichang He, Beilong Tang, Boyan Zhang, Jiaoqi Shao, Xiaomin Ouyang, Daniel Nata Nugraha, Bing Luo
Mobile Application Model Aggregation Cross Device Federated Learning Hardware Aware Training

October 12, 2023

Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
Hanhan Zhou, Tian Lan, Guru Venkataramani, Wenbo Ding
Early Stage Convergence Many Parameter Convergence Analysis Heterogeneous Federated Learning Model Reduction Cross Device Federated Learning

May 20, 2023

Can Public Large Language Models Help Private Cross-device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, H. Brendan McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer
Large Scale Language Model Data Distribution Privacy Utility Private Model Cross Device Federated Learning User Level Differential Privacy

April 14, 2023

TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
Tuo Zhang, Lei Gao, Sunwoo Lee, Mi Zhang, Salman Avestimehr
Asynchronous Federated Learning Cross Device Federated Learning Slow Convergence Stale Gradient Partial Model Training

March 23, 2023

FS-Real: Towards Real-World Cross-Device Federated Learning
Daoyuan Chen, Dawei Gao, Yuexiang Xie, Xuchen Pan, Zitao Li, Yaliang Li, Bolin Ding, Jingren Zhou
Cross Device Federated Learning Heterogeneous Device

March 13, 2023

Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection
Tong Xia, Jing Han, Abhirup Ghosh, Cecilia Mascolo
COVID 19 Detection Data Imbalance Initial Study Mobile Health Diagnostic Model Cross Device Federated Learning

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

February 1, 2023

DoCoFL: Downlink Compression for Cross-Device Federated Learning
Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Y. Levy
Compression Technique Cross Device Federated Learning Dynamic Bandwidth

November 24, 2022

Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
Ji Liu, Juncheng Jia, Beichen Ma, Chendi Zhou, Jingbo Zhou, Yang Zhou, Huaiyu Dai, Dejing Dou
Decentralized Data Dynamic Scheduling Cross Device Federated Learning Federated Multi Task Learning

November 8, 2022

Enhancing Efficiency in Multidevice Federated Learning through Data Selection
Fan Mo, Mohammad Malekzadeh, Soumyajit Chatterjee, Fahim Kawsar, Akhil Mathur
Data Selection Resource Constrained Device Enhancing Efficiency Cross Device Federated Learning Benchmarking Deep Learning Constrained Device

July 26, 2022

Reconciling Security and Communication Efficiency in Federated Learning
Karthik Prasad, Sayan Ghosh, Graham Cormode, Ilya Mironov, Ashkan Yousefpour, Pierre Stock
Federated Learning Secure Aggregation Communication Efficiency Cross Device Federated Learning

June 26, 2022

Cross-Silo Federated Learning: Challenges and Opportunities
Chao Huang, Jianwei Huang, Xin Liu
LeArning Abstract Machine Learning Model Technical Challenge Emerging Opportunity Cross Device Federated Learning Cross Silo Federated Learning

April 27, 2022

FedShuffle: Recipes for Better Use of Local Work in Federated Learning
Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael Rabbat
Training Sample Cross Device Federated Learning Local Construction