Introduction:In order to explore the heterogeneity in federated learning and promote the development of federated learning, Alibaba DAMO Academy Intelligent Computing Laboratory and Tianchi jointly held "CIKM 2022 AnalytiCup Competition: Federation Heterogeneous Task Learning"We look forward to using this competition to help break the "data island" in real applications and promote the sharing of data value.

CIKM 2022 AnalytiCup Competition: Federal Heterogeneous Task Learning

Federated Learning is a new machine learning paradigm that allows multiple participants to jointly train machine learning models without directly sharing their respective data.Its core challenge is how to deal with the heterogeneity between participants (Heterogeneity), among which the heterogeneity of data distribution (non-IID) has attracted extensive attention in the research community and has quickly become one of the research hotspots in the field of federated learning.one.However, in many practical applications of federated learning, the heterogeneity among the participants in the federated task is often more complex and challenging: not only the data distribution, but even the tasks of the participants will exhibit large differences.For example, in a molecular graph federated learning task, the goal of some participants is to judge the type of molecules, that is, a classification task, and the goal of other participants is to predict the strength of molecular chemical properties, that is, a regression task.In this task scenario, although the participants all require the trained model to have the ability to understand the representation of molecular graphs, the specific learning goals are completely different and more challenging than the heterogeneity of the data distribution.

In order to meet the above-mentioned challenges of federated learning in real-world applications, the Intelligent Computing Laboratory of Alibaba DAMO Academy proposed a new federated learning setting: Federated hetero-task learning.Compared with traditional federated learning, this setting encourages researchers to combine federated learning with multi-task learning (Multi-task learning), model pre-training (Model pre-training), automatic machine learning (Auto-ML) and other fieldsIt integrates the research concepts of the real-world applications, so as to open up the "data silos" in practical applications, and finally realize the sharing of data value.At the same time, the Intelligent Computing Laboratory of DAMO Academy designed and implemented an open-source federated learning platform-FederatedScope[1,2] to help researchers more easily explore, design, and implement federated heterogeneous task learning algorithms, and fully implement them.ground verification.

At the same time, the Intelligent Computing Laboratory of Alibaba DAMO Academy and Tianchi jointly held the "CIKM 2022 AnalytiCup Competition: Federal Heterogeneous Task Learning" competition and prepared generous rewards. We look forward to more students participating in the Commonwealth Heterogeneous Task.In the exploration of task learning.

In order to help contestants get started as soon as possible, we have prepared a detailed tutorial

• Official website of the competition: https://tianchi.aliyun.com/competition/entrance/532008/introduction
• FederatedScope Open Source Federated Learning Platform: https://github.com/alibaba/FederatedScope

• Contest prize money:

  • First Place: 5000 USD
  • Second Place: 3000 USD
  • Third Place: 1500 USD
  • Fourth-Tenth: $500 each

• Competition Certificate:

  • Number 1 to 20: Certificate of Ranking
  • Other: Certificate of Entry

• Competition schedule (all times are calculated at 11:59PM UTC on the deadline)

  • July 15, 2022: The competition starts, and the dataset and code are available for download.Forum and tournament leaderboards are starting to update.
  • September 01, 2022: Contest registration closes.
  • September 11, 2022: Deadline for submission of forecast results.
  • September 12, 2022: The organizer conducts a code review, and the top 30 teams on the leaderboard will automatically enter the code review stage.
  • September 18, 2022: Announcement of code review results.
  • September 21, 2022: "CIKM 2022 AnalytiCup Competition" results announced.
  • October 17, 2022: The CIKM 2022 conference begins.

Citation

1. FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. arXiv preprint 2022. pdf
2. FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning. KDD 2022. pdf

Original link:https://developer.aliyun.com/article/991177?a>

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