{
  "id": "2409.11240",
  "version": "v1",
  "published": "2024-09-17T14:42:49.000Z",
  "updated": "2024-09-17T14:42:49.000Z",
  "title": "Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis",
  "authors": [
    "Yipeng Liang",
    "Qimei Chen",
    "Hao Jiang"
  ],
  "comment": "due to the limitation The abstract field cannot be longer than 1,920 characters\", the abstract appearing here is slightly shorter than that in the PDF file",
  "categories": [
    "cs.LG",
    "cs.DC"
  ],
  "abstract": "With the emergence of integrated sensing, communication, and computation (ISCC) in the upcoming 6G era, federated learning with ISCC (FL-ISCC), integrating sample collection, local training, and parameter exchange and aggregation, has garnered increasing interest for enhancing training efficiency. Currently, FL-ISCC primarily includes two algorithms: FedAVG-ISCC and FedSGD-ISCC. However, the theoretical understanding of the performance and advantages of these algorithms remains limited. To address this gap, we investigate a general FL-ISCC framework, implementing both FedAVG-ISCC and FedSGD-ISCC. We experimentally demonstrate the substantial potential of the ISCC framework in reducing latency and energy consumption in FL. Furthermore, we provide a theoretical analysis and comparison. The results reveal that:1) Both sample collection and communication errors negatively impact algorithm performance, highlighting the need for careful design to optimize FL-ISCC applications. 2) FedAVG-ISCC performs better than FedSGD-ISCC under IID data due to its advantage with multiple local updates. 3) FedSGD-ISCC is more robust than FedAVG-ISCC under non-IID data, where the multiple local updates in FedAVG-ISCC worsen performance as non-IID data increases. FedSGD-ISCC maintains performance levels similar to IID conditions. 4) FedSGD-ISCC is more resilient to communication errors than FedAVG-ISCC, which suffers from significant performance degradation as communication errors increase.Extensive simulations confirm the effectiveness of the FL-ISCC framework and validate our theoretical analysis.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-09-17T14:42:49.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "performance analysis",
      "integrated sensing",
      "negatively impact algorithm performance",
      "federated learning",
      "errors negatively impact algorithm"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}