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FedAssess: analysis for efficient communication and security algorithms over various federated learning frameworks and mitigation of label-flipping attack

Anusuya R., Karthika Renuka Dhanaraj

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 3

art. no. e148944

EN

Federated learning is an upcoming concept used widely in distributed machine learning. Federated learning (FL) allows a large number of users to learn a single machine learning model together while the training data is stored on individual user devices. Nonetheless, federated learning lessens threats to data privacy. Based on iterative model averaging, our study suggests a feasible technique for the federated learning of deep networks with improved security and privacy. We also undertake a thorough empirical evaluation while taking various FL frameworks and averaging algorithms into consideration. Secure multi party computation, secure aggregation, and differential privacy are implemented to improve the security and privacy in a federated learning environment. In spite of advancements, concerns over privacy remain in FL, as the weights or parameters of a trained model may reveal private information about the data used for training. Our work demonstrates that FL can be prone to label-flipping attack and a novel method to prevent label-flipping attack has been proposed. We compare standard federated model aggregation and optimization methods, FedAvg and FedProx using benchmark data sets. Experiments are implemented in two different FL frameworks – Flower and PySyft and the results are analyzed. Our experiments confirm that classification accuracy increases in FL framework over a centralized model and the model performance is better after adding all the security and privacy algorithms. Our work has proved that deep learning models perform well in FL and also is secure.

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Uczenie maszynowe z ochroną prywatności danych

Sobolak Kamil, Chołda Piotr

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2023

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nr 4

113--116

PL

Porównano różne metody służące do zapewniania prywatności w przypadku przetwarzania danych z użyciem uczenia maszynowego. Wybrano najbardziej adekwatne metody: szyfrowanie homomorficzne, prywatność różnicowa, metoda uczenia federacyjnego. Efektywność przedstawionych algorytmów została ujęta ilościowo za pomocą powszechnie używanych metryk: funkcji kosztu dla jakości procesu uczenia, dokładności dla klasyfikacji i współczynnika determinacji dla regresji.

EN

Various methods for ensuring privacy in machine learning based data processing were compared. The most suitable methods have been selected: homomorphic encryption, differential privacy, and federated learning. The effectiveness of the presented algorithms was quantified using commonly used metrics: cost function for the quality of the learning process, accuracy for classification, and coefficient of determination for regression.

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Analiza wpływu ataków zatruwających na detekcję zasobów radiowych bazujących na uczeniu federacyjnym

Wasilewska Małgorzata, Bogucka Hanna

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2023

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nr 4

198--203

PL

W niniejszym artykule przedstawiono wyniki badań i analizy wpływu ataków zatruwających odwracających etykiety (ang. label-flipping) na uczenie federacyjne w zastosowaniu dla detekcji zajętości zasobów radiowych. Badania przeprowadzono zarówno dla ataków skoordynowanych jak i losowych, przy zmiennym stosunku liczby użytkowników atakujących do liczby użytkowników uczciwych oraz różnym stopniu agresywności i czasie trwania ataków. Badania skupiają się na porównaniu skuteczności algorytmu detekcji zasobów radiowych przed i po przeprowadzonych atakach.

EN

This paper presents the research results and analysis of the impact of poisoning label-flipping attacks on federated learning for spectrum sensing. The experiments have been executed for random and coordinated attacks for varying attackers-to-genuine-users ratios, different levels of aggressiveness, and time duration of attacks. The results have been obtained by comparing spectrum sensing machine learning model performance with and without attacks.

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Federated learning for Spanish ports as an aid to digitization

González Cancelas Nicoleta, Molina Serrano Beatriz, Soler Flores Francisco

Journal of KONBiN

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2021

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Vol. 51, iss. 2

1--17

EN

The Spanish Port System is immersed in the process of digital transformation towards the concept of Ports 4.0. This entails new regulatory and connectivity requirements, making it necessary to implement the new technologies offered by the market towards digitalization. The digitalization of the individual processes in a first step helps the exchange of digital information between the members of the port community. The next step will mean that the information flow between the participants of a port community is done in a reliable, efficient, paperless way, and thanks to technologies. However, for the Spanish port sector, data exchange has a competitive disadvantage. That is why Federated Learning is proposed. This approach allows several organizations in the port sector to collaborate in the development of models, but without the need to directly share sensitive port data among themselves. Instead of gathering data on a single server, the data remains locked on your server, and the algorithms and predictive models travel between them. The goal of this approach is to benefit from a large set of data, which contributes to increased Machine Learning performance while respecting data ownership and privacy. Through an Inter-institution or "Crosssilo FL" model, different institutions contribute to the training with their local datasets in which different companies collaborate in training a learning machine for the discovery of patterns in private datasets of high sensitivity and high content. This environment is characterized by a smaller number of participants than the mobile case, with typically better bandwidth and less intermittency.