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Found 7 papers, 1 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.
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.
Loosely Coupled Federated Learning Over Generative Models
This paper proposes Loosely Coupled Federated Learning (LC-FL), a framework using generative models as transmission media to achieve low communication cost and heterogeneous federated learning.
