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Federated Learning Systems: Towards Next-generation AI

FLSys 2020

URL: https://sites.google.com/view/flresearch/home
Call For Paper Type: BookChapter
H2 Index: 0
Submission Date: 2020-08-31
Notification Date: 2020-09-30
Final Version Date: 2020-10-31

CALL FOR BOOK CHAPTERS
Springer's Series on Studies in Computational Intelligence
Book Title: Federated Learning Systems: Towards Next-generation AI
The centralized training of deep learning models not only incurs high communication cost of data transfer into the cloud systems but it also raises the privacy-protection concerns of data providers. Federated learning is the new approach whereby centralized deep learning models are distributed across the devices and systems near the data sources which perform initial training and report the updated model attributes to the centralized cloud servers. These centralized servers perform secure and privacy preserving attributes-aggregation and update the global learning model. Federated learning ensures communication efficiency because the raw data never leaves the premises of data providers. In addition, secure federated aggregation benefits in terms of privacy-preservation where neither the centralized servers nor other data providers in the federated learning systems can discriminate the actual data. Still, federated learning systems face key challenges that may hinder their massive adoption. These challenges include privacy, latency, decentralization, asynchronization, personalization, fairness, and bandwidth-optimization, to name a few. This book aims at congregating researchers and practitioners to share their research in showing how federated learning can transform next-generation artificial intelligence applications, and propose solutions to address key federated learning challenges. We are interested in both survey and original works in unexplored and/or emerging topics in the broad area of federated learning systems, architectures, applications, and algorithms, and in novel findings and/or new insights that build on existing works. Our topics of interest include but not limited to:
- Differential Privacy Techniques
- Latency-minimal Federated Learning Applications
- Bandwidth-Optimization Techniques for Efficient Data Communication
- Local and Global Model Personalization
- Decentralized Model Training
- Fine-grained Federated Learning
- Incentive Mechanisms for Large-scale Data Providers
- Trust Models in Federated Learning Systems
- Reputation Models in Federated Learning Systems
- Active Monitoring for Secure and Quality Model Aggregation
- Heterogeneity-Awareness Across Federated Learning Systems
- Context-Awareness for Data Collection, Model Training, and Aggregation
- Model Compression
- Adaptive Model Aggregation
- Fairness (Algorithmic, Systematic)
Important Dates
Abstract Submission Due: May 30, 2020
Abstract Notification Due: June 15, 2020
Full Chapter Submission Due : August 31, 2020
Full Chapter Acceptance Notification: September 30, 2020
Camera-Ready Chapter Submission: October 31, 2020
Book Publication: December 31, 2020
Submission Guidelines
- Authors should only submit original work that has neither appeared elsewhere for publication, nor is presently under review for another refereed publication. Extensions of previously published works are welcome as long as the contributions made in the extended version are significant to warrant publication.
- Authors are expected to submit their chapter proposals which should include abstract and proposed outline by the specified deadline of May 30, 2020 at the following addresses
- Muhammad Habib ur Rehman (mhrehman@ieee.org) - Mohamed Medhat Gaber (mohamed.m.gaber@gmail.com)
- The full chapter submission must be at least 15 pages and at most 30 pages long, including tables, references, figures and appendices, if any. Full chapter MUST be prepared in Latex using following Author Guidelines and it should be submitted via EasyChair at: https://easychair.org/conferences/?conf=flsys2020.
- Each full chapter submission will be independently reviewed by at least three members of the Expert Technical Program Committee (ETPC). They will be evaluated according to the novelty, originality and applicability of the proposed manuscript. The authors of each submission, successful or otherwise, will be notified by September 30, 2020
- What to submit? You must submit a ZIP file via EasyChair containing the items described in the section "manuscript submission checklist" (Instructions for Authors) by the specified deadline of August 31, 2020
- Important Note: Authors are expected to submit the latex files only (i.e. ,tex, .bib, and/or supporting PDF and Graphics).
Editorial Team
Muhammad Habib ur Rehman, PhD
Khalifa University of Science and Technology, United Arab Emirates
mhrheman@ieee.org
https://sites.google.com/site/drmhr2017/home
Mohamed Medhat Gaber, PhD
Professor of Data Analytics,
Birmingham City University, United Kingdom
mohamed.m.gaber@gmail.com
https://mohamedmgaber.weebly.com/

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2024-06-02 ~ 2024-06-06

2024-08-19 ~ 2024-08-22

Federated Learning Systems: Towards Next-generation AI

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