Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitable for edge devices because retraining or finetuning is needed with limited personal data. Also, a full model might be too heavy for edge devices with limited resources. Unfortunately, model compression methods which can handle the model complexity issue also require the retraining phase. These multiple training phases generally need huge computational cost during on-device learning which can be a burden to edge devices. In this work, we propose a dynamic personalization method called prototype-based personalized pruning (PPP). PPP considers both ends of personalization and model efficiency. After training a network, PPP can easily prune the network with a prototype representing the characteristics of personal data and it performs well without retraining or finetuning. We verify the usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.
Personalized federated prototype learning in mixed heterogeneous data scenarios
Guangxi Normal University（广西师范大学）；Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, GuangXi Normal University（教育区块链与智能技术重点实验室）；Hefei University of Technology（合肥工业大学）
Personalized Artificial General Intelligence (AGI) via Neuroscience-Inspired Continuous Learning Systems
Department of AI and Robotics, IGNITE Pathways（人工智能与机器人系，IGNITE路径）；Department of Youth Wellness and Research, Neuro Health Alliance（青年健康与研究系，神经健康联盟）；Medical Director of JFK Neuroscience Sleep Center, HMH-JFK Neuroscience Institute（JFK神经睡眠中心医疗总监，HMH-JFK神经科学研究所）
Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Statistics, Renmin University of China（中国人民大学统计学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）
FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
Beijing Key Laboratory of MEMS Technology and Device Reliability for Industrial Internet, the School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（北京工业互联网MEMS技术与器件可靠性重点实验室，智能工程与自动化学院，北京邮电大学）；School of Business Administration, Southwestern University of Finance and Economics（西南财经大学商学院）；Institute of Artificial Intelligence and the School of Computer Science, Beihang University（北京航空航天大学人工智能研究院和计算机学院）；School of Electronic Engineering, Dublin City University（都柏林城市大学电子工程学院）；DreamSoul
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective
The University of Queensland（昆士兰大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Griffith University（格里菲斯大学）；University of Electronic Science and Technology of China（电子科技大学）
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
School of Computing, Macquarie University（麦考瑞大学计算学院）；School of Information and Physical Sciences, University of Newcastle（纽卡斯尔大学信息与物理科学学院）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；College of Computer Science and Technology, China University of Petroleum (East China)（中国石油大学（华东）计算机科学与技术学院）
