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Found 7 papers, 0 papers with code
Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy.
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices
To tackle the challenge, in this paper, a federated dropout (FedDrop) scheme is proposed building on the classic dropout scheme for random model pruning.
Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels
Targeting broadband channels, we consider the joint control of parameter allocation, sub-channel allocation, and transmission power to improve the performance of PARTEL.
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates.
Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning
To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets.
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning
Specifically, the metrics that measure data importance in active learning (e. g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.
