Closing the Alignment-Maturity Gap in Federated Prototype Learning

• URL: http://arxiv.org/abs/2606.02172v1
• Date: Mon, 01 Jun 2026 12:30:53 GMT
• Title: Closing the Alignment-Maturity Gap in Federated Prototype Learning
• Abstract summary: FedSAP is a framework that stabilises federated representation learning through two complementary mechanisms.<n> Experiments show gains of up to 4 percentage points over the prototype-based baselines evaluated.
• Score: 42.18063554577504
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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