{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T01:54:58Z","timestamp":1783475698521,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Straggler synchronization is a dominant wall-clock bottleneck in synchronous wireless federated learning (FL). Under non-IID data, however, aggressively sampling only fast clients may significantly slow convergence due to statistical heterogeneity. This paper studies PASS-enabled FL, where a radiating pinching antenna (PA) can be activated at an arbitrary position along a dielectric waveguide to reshape uplink latencies. We consider a joint optimization of PA placement and client participation to minimize a proxy for time-to-accuracy, coupling the exact expected maximum round latency via order statistics with a heterogeneity-aware statistical-efficiency proxy. We derive first-order optimality conditions that reveal an explicit tail-latency premium in the KKT recursion, quantifying how latency gaps are amplified by maximum-order-statistic synchronization. Under a latency-class structure, we obtain a within-class square-root sampling law and establish a two-class phase transition where slow-class participation collapses under an explicit heterogeneity-threshold condition as the per-round sample size grows. For PA placement, we prove a piecewise envelope-derivative characterization and provide an exact breakpoint-and-root candidate-enumeration procedure. Simulation results validate the structural findings and show that PASS enables more eligible participation, yielding higher wall-clock accuracy.<\/jats:p>","DOI":"10.3390\/e28030341","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:14:05Z","timestamp":1773843245000},"page":"341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tail-Latency-Aware Federated Learning with Pinching Antenna: Latency, Participation, and Placement"],"prefix":"10.3390","volume":"28","author":[{"given":"Yushen","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiguo","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"ref_1","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.y. 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