{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T21:36:27Z","timestamp":1770845787721,"version":"3.50.1"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,5]]},"DOI":"10.1109\/smc58881.2025.11342605","type":"proceedings-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:54:44Z","timestamp":1769633684000},"page":"1391-1398","source":"Crossref","is-referenced-by-count":0,"title":["Layer-wise Adaptive Compression Method under Non-IID Settings for Federated Learning"],"prefix":"10.1109","author":[{"given":"Ziyuan","family":"Feng","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology,School of Cyber Science and Engineering,Nanjing,China"}]},{"given":"Zijun","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology,School of Cyber Science and Engineering,Nanjing,China"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology,School of Cyber Science and Engineering,Nanjing,China"}]},{"given":"Zhihao","family":"Qu","sequence":"additional","affiliation":[{"name":"Hohai University,Laboratory of Water Big Data Technology of Ministry of Water Resources,Nanjing,China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. of International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3352628"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i21.30400"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2025.3535957"},{"key":"ref5","article-title":"QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding","author":"Alistarh","year":"2017","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"ref6","article-title":"signSGD: Compressed optimisation for non-convex problems","volume-title":"Proc. of the International Conference on Machine Learning (ICML)","author":"Bernstein"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2024.3477972"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00220"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"key":"ref11","article-title":"Federated learning with compression: Unified analysis and sharp guarantees","volume-title":"Proc. of International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Haddadpour"},{"key":"ref12","article-title":"Efficient model compression for hierarchical federated learning","author":"Zhu","year":"2024"},{"key":"ref13","article-title":"Adaptive compression in federated learning via side information","volume-title":"Proc. of the International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Isik"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3382776"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26023"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3233\/faia200253"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5793"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2025.111223"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3073112"},{"key":"ref20","article-title":"Anchor Sampling for Federated Learning with Partial Client Participation","volume-title":"Proc. of the International Conference on Learning Representations (ICLR)","author":"Wu"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3250513"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3233\/FAIA240816"},{"issue":"50","key":"ref23","first-page":"1","article-title":"PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark","volume":"26","author":"Zhang","year":"2025","journal-title":"Journal of Machine Learning Research"}],"event":{"name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","location":"Vienna, Austria","start":{"date-parts":[[2025,10,5]]},"end":{"date-parts":[[2025,10,8]]}},"container-title":["2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11342430\/11342431\/11342605.pdf?arnumber=11342605","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:51:40Z","timestamp":1770843100000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11342605\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,5]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/smc58881.2025.11342605","relation":{},"subject":[],"published":{"date-parts":[[2025,10,5]]}}}