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Found 13 papers, 1 papers with code
Fundamentals of Task-Agnostic Data Valuation
We capture these statistical differences through second moment by measuring diversity and relevance of the seller's data for the buyer; we estimate these measures through queries to the seller without requesting raw data.
Private independence testing across two parties
We introduce $\pi$-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties.
Federated Learning with Downlink Device Selection
At each iteration, the PS broadcasts different quantized global model updates to different participating devices based on the last global model estimates available at the devices.
Blind Federated Edge Learning
At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC).
Communicate to Learn at the Edge
Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research challenges.
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks.
Convergence of Federated Learning over a Noisy Downlink
The PS has access to the global model and shares it with the devices for local training, and the devices return the result of their local updates to the PS to update the global model.
Federated Learning With Quantized Global Model Updates
We analyze the convergence behavior of the proposed LFL algorithm assuming the availability of accurate local model updates at the server.
A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices.
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity.
Federated Learning over Wireless Fading Channels
Overall these results show clear advantages for the proposed analog over-the-air DSGD scheme, which suggests that learning and communication algorithms should be designed jointly to achieve the best end-to-end performance in machine learning applications at the wireless edge.
Collaborative Machine Learning at the Wireless Edge with Blind Transmitters
At each iteration of the DSGD algorithm wireless devices compute gradient estimates with their local datasets, and send them to the PS over a wireless fading multiple access channel (MAC).
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
Following this digital approach, we introduce D-DSGD, in which the wireless devices employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over a multiple access channel (MAC).
