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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
ramos, L. & de Almeida, A. (2026). Revisiting Communication in Federated Learning: Parameter Vs. Knowledge-Based Strategies. RECPAd 2026,.
Exportar Referência (IEEE) 
L. D. Ramos and A. M. Almeida,  "Revisiting Communication in Federated Learning: Parameter Vs. Knowledge-Based Strategies", in RECPAd 2026, 2026
Exportar BibTeX 
@misc{ramos2026_1793027850135,
	author = "ramos, L. and de Almeida, A.",
	title = "Revisiting Communication in Federated Learning: Parameter Vs. Knowledge-Based Strategies",
	year = "2026",
	url = "https://recpad2026.estgv.ipv.pt/"
}
Exportar RIS 
TY  - CPAPER
TI  - Revisiting Communication in Federated Learning: Parameter Vs. Knowledge-Based Strategies
T2  - RECPAd 2026
AU  - ramos, L.
AU  - de Almeida, A.
PY  - 2026
UR  - https://recpad2026.estgv.ipv.pt/
AB  - Federated Learning (FL) enables collaborative training without cen-
tralizing sensitive medical data, but conventional weight exchange in-
curs high communication costs and privacy risks. This work compares
parameter-based and knowledge-based strategies for federated fall de-
tection using three public datasets. While unweighted FedAvg achieves
the strongest performance (0.9692 global PR-AUC), no-weight alterna-
tives maintain competitive local results. Notably, soft-label communi-
cation reduces the communication payload from 49,665 parameters to
only two values per round, offering the most efficient trade-off. These
findings demonstrate the potential of lightweight knowledge-sharing for
resource-constrained wearable healthcare systems.
ER  -