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Found 11 papers, 4 papers with code
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy.
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks
To ensure a smooth learning curve and proper calibration of clusters between the server and the clients, FedCode periodically transfers model weights after multiple rounds of solely communicating codebooks.
Intelligent Blockage Recognition using Cellular mmWave Beamforming Data: Feasibility Study
Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity.
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients.
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning
To the best of our knowledge, this is the first federated SER approach, which utilizes self-training learning in conjunction with federated learning to exploit both labeled and unlabeled on-device data.
Federated Self-Training for Semi-Supervised Audio Recognition
Notably, we show that with as little as 3% labeled data available, FedSTAR on average can improve the recognition rate by 13. 28% compared to the fully supervised federated model.
Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks
The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication.
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data are generally either unlabeled or cannot be annotated readily through user interaction.
Multi-task Self-Supervised Learning for Human Activity Detection
We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal.
Learning behavioral context recognition with multi-stream temporal convolutional networks
In this paper, we propose a multi-stream temporal convolutional network to address the problem of multi-label behavioral context recognition.
Relevance As a Metric for Evaluating Machine Learning Algorithms
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical.
