Search Results for author:
Found 9 papers, 3 papers with code
Over-the-Air Federated Edge Learning with Hierarchical Clustering
Our scheme utilizes OTA cluster aggregations for the communication of the MUs with their corresponding IS, and OTA global aggregations from the ISs to the PS.
Hierarchical Over-the-Air Federated Edge Learning
Federated learning (FL) over wireless communication channels, specifically, over-the-air (OTA) model aggregation framework is considered.
Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory
In order to correct for the inherent bias in this approximation, the algorithm retains in memory an accumulation of the outer products that are not used in the approximation.
Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges
The proposed semantic signal processing framework can easily be tailored for specific applications and goals in a diverse range of signal processing applications.
Straggler Mitigation through Unequal Error Protection for Distributed Approximate Matrix Multiplication
Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing.
Image Classification
Distributed, Parallel, and Cluster Computing
Information Theory
Information Theory
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).
Blind Federated Learning at the Wireless Edge with Low-Resolution ADC and DAC
We study collaborative machine learning systems where a massive dataset is distributed across independent workers which compute their local gradient estimates based on their own datasets.
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).
A Note on the Deletion Channel Capacity
We then provide an upper bound on the concatenated deletion channel capacity $C(d)$ in terms of the weighted average of $C(d_1)$, $C(d_2)$ and the parameters of the three channels.
Information Theory Information Theory
