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Found 8 papers, 2 papers with code
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
For better model personalization, we point out that the hard-won personalized models are not well exploited and propose "inherited private model" to store the personalization experience.
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning
For each pseudo-labeled sample, we select positive and negative samples from labeled data instead of unlabeled data to compute contrastive loss.
MrTF: Model Refinery for Transductive Federated Learning
We propose a novel learning paradigm named transductive federated learning (TFL) to simultaneously consider the structural information of the to-be-inferred data.
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Complex teachers tend to be over-confident and traditional temperature scaling limits the efficacy of {\it class discriminability}, resulting in less discriminative wrong class probabilities.
Avoid Overfitting User Specific Information in Federated Keyword Spotting
Federated KWS (FedKWS) could serve as a solution without directly sharing users' data.
Federated Learning with Position-Aware Neurons
The permutation invariance property of neural networks and the non-i. i. d.
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
We advocate the proposed methods could serve as a preliminary try to explore where to privatize for a novel non-iid scene.
Preliminary Steps Towards Federated Sentiment Classification
Automatically mining sentiment tendency contained in natural language is a fundamental research to some artificial intelligent applications, where solutions alternate with challenges.
