Distributed Sensor Networks and Detection Algorithms
128 papers on this topic
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on...
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems
To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency and...
Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networks
Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any...
Robust Over-the-Air Computation with Type-Based Multiple Access
This paper utilizes the properties of type-based multiple access (TBMA) to investigate its effectiveness as a robust approach for over-the-air computation (AirComp) in the presence of Byzantine...
Federated Learning with Flexible Control
Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g., mobile edge...
Optimal Rate Adaption in Federated Learning with Compressed Communications
Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates. Yet the tradeoff between compression and model accuracy in the...
Fast Federated Learning by Balancing Communication Trade-Offs
Federated Learning (FL) has recently received a lot of attention for large-scale privacy-preserving machine learning. However, high communication overheads due to frequent gradient transmissions...
Coordinated Transmit Beamforming for Multi-antenna Network Integrated Sensing and Communication
This paper studies a multi-antenna network integrated sensing and communication (ISAC) system, in which a set of multi-antenna base stations (BSs) employ the coordinated transmit beamforming to...
Training Time Minimization for Federated Edge Learning with Optimized Gradient Quantization and Bandwidth Allocation
Training a machine learning model with federated edge learning (FEEL) is typically time-consuming due to the constrained computation power of edge devices and limited wireless resources in edge...
The Built-In Robustness of Decentralized Federated Averaging to Bad Data
Decentralized federated learning (DFL) enables devices to collaboratively train models over complex network topologies without relying on a central controller. In this setting, local data remains...
Time-Correlated Sparsification for Communication-Efficient Federated Learning
Federated learning (FL) enables multiple clients to collaboratively train a shared model without disclosing their local datasets. This is achieved by exchanging local model updates with the help...
Over-the-Air Computation with DFT-spread OFDM for Federated Edge Learning
In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the edge server (ES)....
Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning
A combination of deep reinforcement learning and supervised learning is proposed for the problem of active sequential hypothesis testing in completely unknown environments. We make no assumptions...
Wireless Federated $k$-Means Clustering with Non-coherent Over-the-Air Computation
In this study, we propose using an over-the-air computation (OAC) scheme for the federated k-means clustering algorithm to reduce the per-round communication latency when it is implemented over a...
Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks
The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of...
Efficient Sensor Scheduling Strategy Based on Spatio-temporal Scope Information Model
In this paper, based on the spatio-temporal correlation of sensor nodes in the Internet of Things (IoT), a Spatio-temporal Scope information model (SSIM) is proposed to quantify the scope valuable...
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping
One of the key challenges towards the deployment of over-the-air federated learning (AirFL) is the design of mechanisms that can comply with the power and bandwidth constraints of the shared...
Orthogonal Approximate Message-Passing for Spatially Coupled Linear Models
Orthogonal approximate message-passing (OAMP) is proposed for signal recovery from right-orthogonally invariant linear measurements with spatial coupling. Conventional state evolution is...