Project Title: FedOBD: Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout

Keywords: Robustness, Privacy-Preservation, Large-Scale Models

Introduction:
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored in a distributed manner, collaborative fault diagnostic model training often needs to leverage federated learning (FL). As the scale of the industrial fault diagnostic models are often large and communication channels in such systems are often not exclusively used for FL model training, existing deployed FL model training frameworks cannot train such models efficiently across multiple institutions. In this project, we develop and deploy the Federated Opportunistic Block Dropout (FedOBD) approach for industrial fault diagnostic model training. By decomposing large-scale models into semantic blocks and enabling FL participants to opportunistically upload selected important blocks in a quantized manner, it significantly reduces the communication overhead while maintaining model performance. Since its deployment in ENN Group in February 2022, FedOBD has served two coal chemical plants across two cities in China to build industrial fault prediction models. It helped the company reduce the training communication overhead by over 70% compared to its previous AI Engine, while maintaining model performance at over 85% test F1 score. To our knowledge, it is the first successfully deployed dropout-based FL approach.

Publications:

1. Yuanyuan Chen, Zichen Chen, Sheng Guo, Yansong Zhao, Zelei Liu, Pengcheng Wu, Chengyi Yang, Zengxiang Li & Han Yu, "Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout," in Proceedings of the 35th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-23), 2023. (Innovative Application of AI Award)
2. Yuanyuan Chen, Zichen Chen, Pengcheng Wu & Han Yu, "FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning," arXiv preprint arXiv:2208.05174, 2022.

Project Title: HACFL: Hierarchical Auctioning in Crowd-based Federated Learning

Keywords: Fairness, Privacy-Preservation, Data Valuation, Trading

Introduction:
In open collaborative federated learning (FL) taking place within a network of participants, anyone can initiate an FL model training task. Participants can either bid to join an FL task, or help refer others in their own networks. Currently, there is a lack of simulation and benchmarking tools to support research in this domain. In this research, we built Hierarchical Auctioning in Crowd-based Federated Learning (HACFL), a benchmark platform which enables simulations of FL networks with any given topology and reputation-aware hierarchical auction-based FL team formation to support research in this domain. It consists of a configurable back-end simulation system and a web-based interactive user interface, allowing end users and researchers to visualize trust-based open collaborative FL training processes. Results show that leveraging such an ecosystem of FL participants not only improves model performance, but also improves social welfare.

Project Title: Contribution-Aware Federated Learning (CAreFL)

Keywords: Explainability, Fairness, Privacy-Preservation

Introduction:
Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications. Existing deployed FL frameworks cannot address the key issues of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this project, we design, develop and deploy the Contribution-Aware Federated Learning (CAreFL) framework for smart healthcare. It provides fair and explainable FL participant contribution evaluation in an efficient and privacy-preserving manner, and optimizes the FL model aggregation approach based on the evaluation results. Since its deployment in Yidu Cloud Technology Inc. in 2021, CAreFL has served 8 well-established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations 2.84 times faster than the best existing approach, and has improved the average accuracy of the resulting models by 2.62% compared to the previous system (which is significant in industrial settings). To our knowledge, it is the first contribution-aware federated learning successfully deployed in the healthcare industry.

Publications:

1. Zelei Liu, Yuanyuan Chen, Yansong Zhao, Han Yu, Yang Liu, Renyi Bao, Jinpeng Jiang, Zaiqing Nie, Qian Xu & Qiang Yang, "Contribution-Aware Federated Learning for Smart Healthcare," in Proceedings of the 34th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22), pp. 12396-12404, 2022. (Innovative Application of AI Award)
2. Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu & Lizhen Cui. GTG-Shapley: Efficient and accurate participant contribution evaluation in federated learning. ACM Transactions on Intelligent Systems and Technology, ACM (2022).

Project Title: CrowdFL: A Marketplace for Crowdsourced Federated Learning

Keywords: Privacy-Preservation, Data Valuation, Trading, Crowdsourcing

Introduction:
Amid data privacy concerns, Federated Learning (FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists a need for a platform that matches data owners (supply) with model requesters (demand). In this project, we built CrowdFL, a platform to facilitate the crowdsourcing of FL model training. It coordinates client selection, model training, and reputation management, which are essential steps for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. To the best of our knowledge, it is the first platform to support crowdsourcing-based FL on edge devices.

Publications:

1. Daifei Feng, Cicilia Helena, Wei Yang Bryan Lim, Jer Shyuan Ng, Hongchao Jiang, Zehui Xiong, Jiawen Kang, Han Yu, Dusit Niyato & Chunyan Miao, "CrowdFL: A Marketplace for Crowdsourced Federated Learning," in Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-22), 2022.

Project Title: FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

Keywords: Privacy-Preservation, Visualization, Process Management

Introduction:
Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this project, we built FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

Publications:

1. Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu & Qiang Yang, Federated learning-powered visual object detection for safety monitoring. AI Magazine, vol. 42, no. 2, AAAI Press (2021).
2. Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu & Qiang Yang, "FedVision: An Online Visual Object Detection Platform Powered by Federated Learning," in Proceedings of the 32nd Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-20), pp. 13172-13179, 2020. (Innovative Application of AI Award)