Personalized Federated Semi-Supervised Learning with Black-Box Models
Author:
Affiliation:
1. Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,College of Computer Science and Technology,Nanjing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10415628/10415561/10415780.pdf?arnumber=10415780
Reference24 articles.
1. Exploiting Unlabeled Data in Smart Cities using Federated Edge Learning
2. Federated semi-supervised learning with inter-client consistency & disjoint learning;Jeong;ICLR,2021
3. Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data
4. Communication-efficient learning of deep networks from decentralized data;McMahan;AISTATS,2017
5. Inferring class label distribution of training data from classifiers: An accuracy-augmented meta-classifier attack;Ramakrishna;CoRR,2022
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