RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment

• URL: http://arxiv.org/abs/2601.14746v2
• Date: Sun, 25 Jan 2026 19:19:18 GMT
• Title: RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment
• Authors: Hongyue Wu, Hangyu Li, Guodong Fan, Haoran Zhu, Shizhan Chen, Zhiyong Feng,
• Abstract summary: Federated learning (FL) enables collaborative model training without sharing raw data in edge environments.<n>We propose RefProtoFL, a communication-efficient FL framework that integrates External-Referenced Prototype Alignment.<n>We show that RefProtoFL attains higher classification accuracy than state-of-the-art prototype-based FL baselines.
• Score: 20.458428841832742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables collaborative model training without sharing raw data in edge environments, but is constrained by limited communication bandwidth and heterogeneous client data distributions. Prototype-based FL mitigates this issue by exchanging class-wise feature prototypes instead of full model parameters; however, existing methods still suffer from suboptimal generalization under severe communication constraints. In this paper, we propose RefProtoFL, a communication-efficient FL framework that integrates External-Referenced Prototype Alignment (ERPA) for representation consistency with Adaptive Probabilistic Update Dropping (APUD) for communication efficiency. Specifically, we decompose the model into a private backbone and a lightweight shared adapter, and restrict federated communication to the adapter parameters only. To further reduce uplink cost, APUD performs magnitude-aware Top-K sparsification, transmitting only the most significant adapter updates for server-side aggregation. To address representation inconsistency across heterogeneous clients, ERPA leverages a small server-held public dataset to construct external reference prototypes that serve as shared semantic anchors. For classes covered by public data, clients directly align local representations to public-induced prototypes, whereas for uncovered classes, alignment relies on server-aggregated global reference prototypes via weighted averaging. Extensive experiments on standard benchmarks demonstrate that RefProtoFL attains higher classification accuracy than state-of-the-art prototype-based FL baselines.

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