FL Systems

• {Lu20} Y. Lu, X. Huang, K. Zhang, S. Maharjan and Y. Zhang, "Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles," in IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4298-4311, April 2020
• {Zhao21} Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, Yingbo Liu. Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices. IEEE Internet of Things Journal, 2021, 8(3):1817-1829.
• {Damaskinos22} Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec, Vlad Nitu, Rhicheek Patra, and Francois Taiani. FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction. ACM Trans. Intell. Syst. Technol., 2022, 13(5):Article 79.
• {Lai22} Fan Lai, Yinwei Dai, Sanjay S. Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, Mosharaf Chowdhury. FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. Proc. of ICML, 2022. FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research [Code|Slides]
• {Ottun23} Abdul-Rasheed Ottun, Pramod C. Mane, Zhigang Yin, Souvik Paul, Mohan Liyanage, Jason Pridmore, Aaron Yi Ding, Rajesh Sharma, Petteri Nurmi, Huber Flores. Social-aware Federated Learning: Challenges and Opportunities in Collaborative Data Training. IEEE Internet Computing, 2023. contribute social-aware federated learning as a solution to boost the contributions of individuals by allowing outsourcing tasks to social connections

FL Applications

• {Feng20} Jie Feng, Can Rong, Funing Sun, Diansheng Guo, and Yong Li. PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2020, 4(1):Article 10.
• {Ouyang21} Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, Guoliang Xing. ClusterFL: A Similarity-Aware Federated Learning System for Human Activity Recognition. Proc. of ACM MobiSys, 2021.
• {Pfitzner21} Bjarne Pfitzner, Nico Steckhan, and Bert Arnrich. Federated Learning in a Medical Context: A Systematic Literature Review. ACM Trans. Internet Technol., 2021, 21, 2, Article 50 (June 2021). an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets
• {Bakopoulou22} E. Bakopoulou, B. Tillman and A. Markopoulou, FedPacket: A Federated Learning Approach to Mobile Packet Classification. IEEE Transactions on Mobile Computing, vol. 21, no. 10, pp. 3609-3628, 1 Oct. 2022. propose a federated learning approach to mobile packet classification, which enables devices to collaboratively train a global model, without uploading the training data collected on devices; showed that Federated achieves a significantly higher F1 score than Local and is comparable to Centralized models, and it does so within a few communication rounds and with minimal computation per user, which is important in the mobile environment