Closing the Alignment-Maturity Gap in Federated Prototype Learning
2026-06-01 • Machine Learning
Machine LearningComputer Vision and Pattern Recognition
AI summaryⓘ
The authors study how to teach computers to recognize images well when data is spread out and very different across many users, which is called Federated Learning. They find that current methods that share summary information between users cause problems early on, making it hard for the computer to learn good distinctions. To fix this, the authors propose a new method called FedSAP that waits to share global information until local learning is stable and uses a special way to keep different classes apart in the learned space. Their approach improves accuracy, especially when data differences are large, and can also handle partly unlabeled data easily.
Federated LearningPrototype-based methodsStatistical heterogeneityRepresentation learningEmbedding spaceNon-IID dataAlignment curriculumProxy separation lossSemi-supervised learningClass clusters
Authors
Mario Casado-Diez, Alejandro Dopico-Castro, Verónica Bolón-Canedo, Bertha Guijarro-Berdiñas
Abstract
Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL). Prototype-based methods address statistical heterogeneity by sharing class-level representations across clients but create a distance-dependent gradient pressure that is particularly severe during early training rounds: alignment pressure applied to immature global prototypes, aggregated from noisy local representations, generates large gradients that suppress the emergence of local discriminative structure. The result is a poorly organized embedding space and degraded recognition performance, particularly under severe non-IID conditions. We propose FedSAP, a framework that stabilises federated representation learning through two complementary mechanisms: a deterministic alignment curriculum that delays global alignment until local representations become stable and a geometry-driven proxy separation loss that enforces inter-class structure on the unit hypersphere using the existing prototype bank without introducing additional parameters or communication overhead. Together, these mechanisms produce compact, well-separated class clusters without altering the underlying communication protocol between federation's participants. Experiments across three benchmarks and varying degrees of heterogeneity show gains of up to 4 percentage points over the prototype-based baselines evaluated, with improvements most pronounced under high heterogeneity. The representational nature of our framework further enables a straightforward extension to semi-supervised settings, where unlabelled data is incorporated with minimal modification, underscoring the generality of scheduled alignment as a design principle.