@inproceedings{prasad-khan-2026-fedpagr,
title = "{F}ed{PAGR}: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures",
author = "Prasad, Kris and
Khan, Md Abdullah Al Hafiz",
editor = "T.Y.S.S., Santosh and
Rodriguez, Juan Diego and
de Gibert, Ona",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-srw.37/",
pages = "420--427",
ISBN = "979-8-89176-393-7",
abstract = "Federated learning with heterogeneous client architectures cannot rely on parameter aggregation. Prototype-based methods address architectural heterogeneity by exchanging class-level representations, but naively averaging prototypes across non-IID clients leads to semantic drift and poor inter-class separation. We propose FedPAGR, a framework where heterogeneous clients project their features into a shared consensus space and exchange class prototypes with a central server. The server refines aggregated prototypes through a geometric regularization objective that enforces agreement with client submissions and inter-class angular separation. Clients anchor their classifiers to the refined prototypes and train with a composite objective combining classification, prototype alignment, and entropy regularization. We evaluate FedPAGR across multiple domains, including four image benchmarks and a clinical NLP task using heterogeneous ClinicalBERT variants, with five architectures per federation under severe label heterogeneity ($\alpha{=}0.1$). FedPAGR achieves the highest ensemble accuracy across all four image datasets and the highest local test accuracy on low-class and clinical tasks, including a 4.99-point improvement over the strongest baseline on MIMIC-IV, while remaining competitive on high-class benchmarks."
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<abstract>Federated learning with heterogeneous client architectures cannot rely on parameter aggregation. Prototype-based methods address architectural heterogeneity by exchanging class-level representations, but naively averaging prototypes across non-IID clients leads to semantic drift and poor inter-class separation. We propose FedPAGR, a framework where heterogeneous clients project their features into a shared consensus space and exchange class prototypes with a central server. The server refines aggregated prototypes through a geometric regularization objective that enforces agreement with client submissions and inter-class angular separation. Clients anchor their classifiers to the refined prototypes and train with a composite objective combining classification, prototype alignment, and entropy regularization. We evaluate FedPAGR across multiple domains, including four image benchmarks and a clinical NLP task using heterogeneous ClinicalBERT variants, with five architectures per federation under severe label heterogeneity (α=0.1). FedPAGR achieves the highest ensemble accuracy across all four image datasets and the highest local test accuracy on low-class and clinical tasks, including a 4.99-point improvement over the strongest baseline on MIMIC-IV, while remaining competitive on high-class benchmarks.</abstract>
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%0 Conference Proceedings
%T FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures
%A Prasad, Kris
%A Khan, Md Abdullah Al Hafiz
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F prasad-khan-2026-fedpagr
%X Federated learning with heterogeneous client architectures cannot rely on parameter aggregation. Prototype-based methods address architectural heterogeneity by exchanging class-level representations, but naively averaging prototypes across non-IID clients leads to semantic drift and poor inter-class separation. We propose FedPAGR, a framework where heterogeneous clients project their features into a shared consensus space and exchange class prototypes with a central server. The server refines aggregated prototypes through a geometric regularization objective that enforces agreement with client submissions and inter-class angular separation. Clients anchor their classifiers to the refined prototypes and train with a composite objective combining classification, prototype alignment, and entropy regularization. We evaluate FedPAGR across multiple domains, including four image benchmarks and a clinical NLP task using heterogeneous ClinicalBERT variants, with five architectures per federation under severe label heterogeneity (α=0.1). FedPAGR achieves the highest ensemble accuracy across all four image datasets and the highest local test accuracy on low-class and clinical tasks, including a 4.99-point improvement over the strongest baseline on MIMIC-IV, while remaining competitive on high-class benchmarks.
%U https://aclanthology.org/2026.acl-srw.37/
%P 420-427
Markdown (Informal)
[FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures](https://aclanthology.org/2026.acl-srw.37/) (Prasad & Khan, ACL 2026)
ACL