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Padua, Italy
Padua, Italy
18-23 July 2022
18-23 July 2022
Centro Congressi, Padova, Italy
IMPORTANT NOTES FOR SUBMISSION DEADLINE EXTENSION!!
According to the other SS and Track of the WCCI 2022 Conference, we are pleasured to confirm that also the deadline for this SS on Federated Learning and Cooperative Neural Networks (CoNN) is extended to the February 14, 2022 (11:59 PM AoE) .
ANYWAY, it's MANDATORY to submit until the January 31, 2022 (11:59 PM AoE) a draft of the final paper including the Title of your Contribution and the Abstract, in order to be be enabled to access to the final submission system that closes on the 14th February.
DO NOT FORGET!!
DO NOT FORGET!!
DO NOT FORGET and do n ot miss the preliminary date of 31st January 2022.
Contributions Draft that will not be submitted in the Conference Manager System until the 31st of January 2022, can't access to the final submission set to the 14th February 2022.
Official Guidelines for prepapring correctly your contribution are provided in the official page of the main hosting conference WCCI 2022, at the following link.
Authors Instructions: https://wcci2022.org/submission/
Official Deadlines: https://wcci2022.org/dates/
Special Session Scope:
Special Session Scope:
Federated Learning (FL) comes to rescue from standard centralized training of machine learning models, that is unfeasible in many interesting use-cases, where the associated data transfer and maintenance costs (e.g., video analytics), privacy concerns (e.g., in healthcare and justice domains settings), or sensitivity of the proprietary data (e.g., in drug discovery) represent a heavy bottleneck. And yet, different parties that own even a small amount of data would benefit from access to accurate models. So, Federated learning allows multiple clients owning data to train shared models collaboratively, under the orchestration of a central server without having to share raw data. Typically, FL proceeds in multiple rounds of communication between the server and the clients: the clients compute model updates on their local data and send them to the server which aggregates and applies these updates to the shared model. While gaining popularity very quickly, FL is a relatively new subfield with many open questions and unresolved challenges, as the Statistical Unbalancing of Data, Distributed Optimization Problems, Communication Latency as far as Security and Resilience to attacks issues, that has attracted increasing interest from the international research communities.
Federated Learning (FL) comes to rescue from standard centralized training of machine learning models, that is unfeasible in many interesting use-cases, where the associated data transfer and maintenance costs (e.g., video analytics), privacy concerns (e.g., in healthcare and justice domains settings), or sensitivity of the proprietary data (e.g., in drug discovery) represent a heavy bottleneck. And yet, different parties that own even a small amount of data would benefit from access to accurate models. So, Federated learning allows multiple clients owning data to train shared models collaboratively, under the orchestration of a central server without having to share raw data. Typically, FL proceeds in multiple rounds of communication between the server and the clients: the clients compute model updates on their local data and send them to the server which aggregates and applies these updates to the shared model. While gaining popularity very quickly, FL is a relatively new subfield with many open questions and unresolved challenges, as the Statistical Unbalancing of Data, Distributed Optimization Problems, Communication Latency as far as Security and Resilience to attacks issues, that has attracted increasing interest from the international research communities.
Main Goals of this Special Session:
Main Goals of this Special Session:
The main objective of the 2022 Special Session on Federated Learning and Distribute Cooperative Learning, hosted in the IEEE International Joint Conference on Neural Networks (IJCNN), among the world most influential international conferences on Neural Networks, is to recall the international research community's attention to the emerging perspective and practical algorithms in Federated Learning and Cooperative Neural Networks A goal that this session aims to reach consists in collecting several novel contributions and research experiences from the Federated Learning studies and applications, from different research communities and concerning different but complementary solutions and proposals to mitigate issues and optimize Federated Learning algorithms. Attention paid to Federated Learning and Cooperative NN has grown significantly in the last few times, thus witnessing a remarkable increase in the research contributions published in the last months of the 2021 year. The expected outcomes are very high quality and novel contributions, to help take a step forward in this interesting but complex area.
The main objective of the 2022 Special Session on Federated Learning and Distribute Cooperative Learning, hosted in the IEEE International Joint Conference on Neural Networks (IJCNN), among the world most influential international conferences on Neural Networks, is to recall the international research community's attention to the emerging perspective and practical algorithms in Federated Learning and Cooperative Neural Networks A goal that this session aims to reach consists in collecting several novel contributions and research experiences from the Federated Learning studies and applications, from different research communities and concerning different but complementary solutions and proposals to mitigate issues and optimize Federated Learning algorithms. Attention paid to Federated Learning and Cooperative NN has grown significantly in the last few times, thus witnessing a remarkable increase in the research contributions published in the last months of the 2021 year. The expected outcomes are very high quality and novel contributions, to help take a step forward in this interesting but complex area.
The main outcomes of the first edition of this special session, held in 2021, were represented by a reinforced interest in the challenges and solutions to the main issue related to distributed learning paradigms as the Federated Learning and the Cooperative Neural Networks and the creation of research collaboration for sharing findings and building something better than the existent.
The main outcomes of the first edition of this special session, held in 2021, were represented by a reinforced interest in the challenges and solutions to the main issue related to distributed learning paradigms as the Federated Learning and the Cooperative Neural Networks and the creation of research collaboration for sharing findings and building something better than the existent.
So, we were encouraged to propose once again this special session in order to provide the opportunity to share findings, advances with the past participants and new researchers.
So, we were encouraged to propose once again this special session in order to provide the opportunity to share findings, advances with the past participants and new researchers.
Topics of the S.S. include (but are not limited to):
Topics of the S.S. include (but are not limited to):
- Advances, novel issues, and open challenges in Federated Learning
- Advances, novel issues, and open challenges in Federated Learning
- Optimization of Distributed Consensus Strategies
- Optimization of Distributed Consensus Strategies
- Federated Learning Trust Policy and Strategies
- Federated Learning Trust Policy and Strategies
- Resilient and Trustworthy Federated Learning
- Resilient and Trustworthy Federated Learning
- Security Concerns with Federated Learning
- Security Concerns with Federated Learning
- Poisoning Attacks and Countermeasures in Reliable Federated Learning
- Poisoning Attacks and Countermeasures in Reliable Federated Learning
- Resilience Issues in Federated Learning
- Resilience Issues in Federated Learning
- Resilience, Robustness, Reliability and Trustworthiness Issues in Federated Learning Models
- Resilience, Robustness, Reliability and Trustworthiness Issues in Federated Learning Models
- Performance Evaluation Methods, Metrics and Tools of Federated Learning Systems
- Performance Evaluation Methods, Metrics and Tools of Federated Learning Systems
- Performance Optimization of Federated Learning Models
- Performance Optimization of Federated Learning Models
- Privacy Concerns and Federated Learning
- Privacy Concerns and Federated Learning
- Case Studies and Applications of Federated Learning
- Case Studies and Applications of Federated Learning
- Federated Learning frameworks and tools employment and comparisons.
- Federated Learning frameworks and tools employment and comparisons.
- Federated Learning and Blockchain
- Federated Learning and Blockchain
- Federated Learning For IoT
- Federated Learning For IoT
- Federated Learning For Smart Grids
- Federated Learning For Smart Grids
- Federated Learning For Energy Efficiency In IoT
- Federated Learning For Energy Efficiency In IoT
- Federated Learning For Industrial Applications
- Federated Learning For Industrial Applications
- Federated Learning and Graph-based Approaches for Fraud Detection
- Federated Learning and Graph-based Approaches for Fraud Detection
- Federated Learning For Intrusion Detection In IoT
- Federated Learning For Intrusion Detection In IoT
- Federated Learning With Edge Computing For Cybersecurity In IoT
- Federated Learning With Edge Computing For Cybersecurity In IoT
- Federated Learning For Privacy Preservation Of The Users In Social Media Apps
- Federated Learning For Privacy Preservation Of The Users In Social Media Apps
Special Session Chairs: Dr. Fiammetta Marulli and Dr. Francesco Mercaldo
Contact and Information about this Special Session can be also asked to: fiammetta.marulli@unicampania.it
Contact and Information about this Special Session can be also asked to: fiammetta.marulli@unicampania.it
Here is more detailed information about deadlines and submission instructions:
Here is more detailed information about deadlines and submission instructions:
- Submission Link : WCCI 2022 adopts Microsoft CMT as submission system, available at the following link: https://cmt3.research.microsoft.com/IEEEWCCI2022/
- Submission Link : WCCI 2022 adopts Microsoft CMT as submission system, available at the following link: https://cmt3.research.microsoft.com/IEEEWCCI2022/
Papers submitted for special sessions will be peer-reviewed in the same way as submissions to the regular sessions.
Papers submitted for special sessions will be peer-reviewed in the same way as submissions to the regular sessions.
Important Dates:
Important Dates:
Paper Submission Deadline: January 31, 2022 (11:59 PM AoE) STRICT DEADLINE
Paper Submission Deadline: January 31, 2022 (11:59 PM AoE) STRICT DEADLINE
Notification of Acceptance: April 26, 2022
Notification of Acceptance: April 26, 2022
Camera-ready Submission: May 23, 2022
Camera-ready Submission: May 23, 2022
Paper Submission:
Paper Submission:
Prospective authors are invited to submit complete papers of no more than eight (8) pages in IEEE two-column conference proceedings format. Authors should submit their papers in PDF through the online submission system, which will be available at
Prospective authors are invited to submit complete papers of no more than eight (8) pages in IEEE two-column conference proceedings format. Authors should submit their papers in PDF through the online submission system, which will be available at
Submission Link : WCCI 2022 adopts Microsoft CMT as submission system, available ath the following link: https://cmt3.research.microsoft.com/IEEEWCCI2022/
Submission Link : WCCI 2022 adopts Microsoft CMT as submission system, available ath the following link: https://cmt3.research.microsoft.com/IEEEWCCI2022/
Detailed information and complete authors' instructions are provided in the official WCCI 2022 Conference page at: https://wcci2022.org/submission/
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