From centralized to Federated Learning: Exploring performance and end-to-end resource consumption

Drainakis, Georgios; Pantazopoulos, Panagiotis; Κατσαρός, Κωνσταντίνος; Sourlas, Vasilis; Amditis, Angelos; Kaklamani, Dimitra I.

doi:10.1016/j.comnet.2023.109657

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…As well as inadequate MV handling, recent studies also disregarded the single point of failure (SPoF) vulnerability, which could bring the entire forecasting system down when the server hosting the centralized ML architecture is offline [14]. Although existing distributed ML architectures could solve this [15], they are inefficient, and the data heterogeneity could negatively impact the accuracy [16].…”

Section: Introductionmentioning

confidence: 99%

Resilient Electricity Load Forecasting Network with Collective Intelligence Predictor for Smart Cities

Bin Kamilin,

Yamaguchi

2024

Electronics

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Accurate electricity forecasting is essential for smart cities to maintain grid stability by allocating resources in advance, ensuring better integration with renewable energies, and lowering operation costs. However, most forecasting models that use machine learning cannot handle the missing values and possess a single point of failure. With rapid technological advancement, smart cities are becoming lucrative targets for cyberattacks to induce packet loss or take down servers offline via distributed denial-of-service attacks, disrupting the forecasting system and inducing missing values in the electricity load data. This paper proposes a collective intelligence predictor, which uses modular three-level forecasting networks to decentralize and strengthen against missing values. Compared to the existing forecasting models, it achieves a coefficient of determination score of 0.98831 with no missing values using the base model in the Level 0 network. As the missing values in the forecasted zone rise to 90% and a single-model forecasting method is no longer effective, it achieves a score of 0.89345 with a meta-model in the Level 1 network to aggregate the results from the base models in Level 0. Finally, as missing values reach 100%, it achieves a score of 0.81445 by reconstructing the forecast from other zones using the meta-model in the Level 2 network.

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Section: Introductionmentioning

confidence: 99%

Resilient Electricity Load Forecasting Network with Collective Intelligence Predictor for Smart Cities

Bin Kamilin,

Yamaguchi

2024

Electronics

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Federated learning for biometric recognition: a survey

Guo,

Mu,

Liu

et al. 2024

Artif Intell Rev

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Recently, deep learning (DL) has achieved great success in biometric recognition. The application of DL has also led to a high demand for biometric data. However, as people attach more importance to privacy protection, biometric data have become increasingly difficult to obtain and access, leading to significant limitations in the development and application of DL-based biometric recognition. Federated learning (FL), a distributed learning technique with privacy protection, provides a potential solution to this problem. Several researchers have attempted to integrate FL into biometric recognition. These studies have shown that the introduction of FL not only solves the conflict between privacy and accessibility of biometric data but also improves the accuracy and generalizability of local recognition systems. Therefore, the combination of FL and biometric recognition techniques has become a new research hotspot. In this survey, we comprehensively review the latest advances regarding the application of FL to biometric recognition, biometric presentation attack detection and the related fields to provide new researchers with a quick and systematic overview of this emerging cross-disciplinary field. This paper also summarizes the future opportunities and challenges of this field. To our knowledge, this is the first survey that systematically organizes and analyses federated biometric recognition and related fields to provide suggestions and references for future research.

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