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de Gusmao

As seen in: arXiv, Frontiers, Revista Brasileira de Engenharia Agricola e Ambiental, Neurology Journal
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Articles by de Gusmao

Omni-C: Compressing Heterogeneous Modalities into a Single Dense Encoder

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By Kin Wai, de Gusmao, Pedro Porto Buarque
| arXiv
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CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments

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By Minhoe Kim, de Gusmao, Dongjin Ji, Byoungjun Park
| arXiv
arXiv:2511.11778 (cs) View PDF HTML (experimental) Comments: Subjects: Machine Learning (cs.LG) MSC classes: 68T05 Cite as: arXiv:2511.11778 [cs.LG] (or arXiv:2511.11778v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2511.11778 Submission history From: Minhoe Kim [ view email] [v1] Fri, 14 Nov 2025 13:16:52 UTC (1,127 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle...
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Diagnostic approach to Episodic Ataxia types 1 and 2: a proposed algorithm for limited resource-settings

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By de Gusmao, São Paulo, Della Ripa
| Frontiers
BRIEF RESEARCH REPORT article Front. Neurol. Sec. Pediatric Neurology Diagnostic approach to Episodic Ataxia types 1 and 2: a proposed algorithm for limited resource-settings 1. Massachusetts General Hospital, Boston, United States 2. Universidade de Sao Paulo, São Paulo, Brazil 3. Instituto Sirio-Libanes de Ensino e Pesquisa, São Paulo, Brazil 4. Pittsford, New York, United States 5. University of Rochester, Rochester, United States 6.
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FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning

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By Kin Wai, de Gusmao, Pedro Porto Buarque
| arXiv
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L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning

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By de Gusmao, Pedro Porto Buarque, Yan Gao, Mina Alibeigi
| arXiv
arXiv:2307.07393 (cs) Download a PDF of the paper titled L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning, by Yasar Abbas Ur Rehman and 5 other authors Download PDF Submission history From: Yan Gao [ view email] [v1] Fri, 14 Jul 2023 15:07:30 UTC (15,633 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart...
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[2306.17453] High-throughput Simulation of Federated Learning via Resource-Aware Client Placement

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By de Gusmao, Pedro Porto Buarque, Nicholas Donald
| arXiv
arXiv:2306.17453 (cs) Download a PDF of the paper titled High-throughput Simulation of Federated Learning via Resource-Aware Client Placement, by Lorenzo Sani and 6 other authors Download PDF Submission history From: Lorenzo Sani [ view email] [v1] Fri, 30 Jun 2023 07:57:30 UTC (1,309 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Links...
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FedVal: Different good or different bad in federated learning

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By de Gusmao, Pedro Porto Buarque
| arXiv
arXiv:2306.04040 (cs) Download a PDF of the paper titled FedVal: Different good or different bad in federated learning, by Viktor Valadi and 4 other authors Download PDF Submission history From: Viktor Valadi [ view email] [v1] Tue, 6 Jun 2023 22:11:13 UTC (2,326 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media DagsHub Toggle DagsHub...
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[2102.07627] A first look into the carbon footprint of federated learning

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By de Gusmao, Pedro Porto Buarque
| arXiv
[Submitted on 15 Feb 2021 ( v1 ), last revised 22 May 2023 (this version, v6)] Title:A first look into the carbon footprint of federated learning Download a PDF of the paper titled A first look into the carbon footprint of federated learning, by Xinchi Qiu and 8 other authors Download PDF Abstract: Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.
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[2305.11236] Efficient Vertical Federated Learning with Secure Aggregation

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By de Gusmao, Pedro Porto Buarque
| arXiv
arXiv:2305.11236 (cs) Download a PDF of the paper titled Efficient Vertical Federated Learning with Secure Aggregation, by Xinchi Qiu and 5 other authors Download PDF Submission history From: Xinchi Qiu [ view email] [v1] Thu, 18 May 2023 18:08:36 UTC (506 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media DagsHub Toggle DagsHub (What is...
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