{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:48:12Z","timestamp":1791953292203,"version":"4.3.4"},"reference-count":88,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Federated learning trains shared models without centralizing local data, but repeated model exchange can become a bottleneck in bandwidth-, energy-, and latency-constrained edge systems. This paper presents FEDCODE, a communication-aware federated learning simulation framework for reproducible evaluation of update representations and compression methods with explicit accounting of communication volume and runtime under controlled training configurations. Experiments on MNIST, CIFAR-10, and LEAF-based federated datasets show that update representation, quantization scale, and coder choice strongly affect payload size, while lightweight integer and entropy coding can substantially reduce communication. Comparisons under a matched protocol with established FL compression methods show favorable communication\u2013quality trade-offs for the evaluated FEDCODE delta-coding configurations. Adaptive coder-parameter tuning provides automatic parameter selection but does not consistently outperform strong fixed configurations. We further propose Coder-Aware Adaptive Delta Scaling (CAADS), which combines update statistics, drift in update magnitude, and coder-specific code-length estimation to adapt the quantization scale during training. CAADS adjusts the communication\u2013quality trade-off: conservative settings preserve model performance, whereas aggressive scaling further reduces payload at the cost of larger quality degradation. Additional experiments on edge devices show that end-to-end compression benefits depend on device capability and network bandwidth. Overall, effective federated learning compression requires joint consideration of payload, model quality, codec cost, and network conditions.<\/jats:p>","DOI":"10.3390\/s26196297","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:57:46Z","timestamp":1791197866000},"page":"6297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FEDCODE: A Framework and Experimental Study of Model-Update Compression for Communication-Efficient Federated Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9285-6858","authenticated-orcid":false,"given":"Emanuel","family":"Guberovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6980-407X","authenticated-orcid":false,"given":"Igor","family":"\u010cavrak","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"ref_1","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017, January 20\u201322). 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