# Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration

arXiv: https://arxiv.org/abs/2206.05949 · HTML: https://mlsanity.com/paper/2206.05949/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2022.

## Built on by (most cited first, 12 of 12)

- [Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing](https://mlsanity.com/paper/2403.12400/) (2024)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [Integrated Sensing-Communication-Computation for Edge Artificial Intelligence](https://mlsanity.com/paper/2306.01162/) (2023)
- [Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI](https://mlsanity.com/paper/2306.06603/) (2023)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2411.08918/) (2024)
- [Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication](https://mlsanity.com/paper/2311.12223/) (2023)
- [Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis](https://mlsanity.com/paper/2409.11240/) (2024)
- [Content Accuracy and Quality Aware Resource Allocation Based on LP-Guided DRL for ISAC-Driven AIGC Networks](https://mlsanity.com/paper/2508.12079/) (2025)
- [Latency-aware Multimodal Federated Learning over UAV Networks](https://mlsanity.com/paper/2510.01717/) (2025)
- [Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks](https://mlsanity.com/paper/2604.10662/) (2026)

## Abstract (arXiv)

In this paper, we address the problem of joint sensing, computation, and communication (SC2) resource allocation for federated edge learning (FEEL) via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing transmit power, exceeding which yields sensing data samples with approximately the same satisfactory quality. Then, the joint SC2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time, energy supply, and sensing quality of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determine the joint sensing and communication resource allocation that maximizes the total number of samples that can be sensed during the entire training process; the second one concerns the partition of the attained total number of sensed samples over all the communication rounds to determine the batch size at each round for convergence speed maximization. The first subproblem on joint sensing and communication resource allocation is converted to a single-variable optimization problem by exploiting the derived relation between different control variables (resources), which thus allows an efficient solution via one-dimensional grid search. For the second subproblem, it is found that the number of samples to be sensed (or batch size) at each round is a decreasing function of the loss function value attained at the round. Based on this relationship, the approximate optimal batch size at each communication round is derived in closed-form as a function of the round index. Finally, extensive simulation results are provided to validate the superiority of the proposed joint SC2 resource allocation scheme.