Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning

Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzatine poisoning adversarial attacks. We argue that the federated learning model has to avoid those kind of adversarial attacks through filtering out the adversarial clients by means of the federated aggregation operator. We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model. We assess it as a defense against adversarial attacks deploying a deep learning classification model in a federated learning setting on the Fed-EMNIST Digits, Fashion MNIST and CIFAR-10 image datasets. The results show that the dynamic selection of the clients to aggregate enhances the performance of the global learning model and discards the adversarial and poor (with low quality models) clients.R&D&I grants - MCIN/AEI, Spain PID-2020-119478GB-I00 PID2020-116118GA-I00 EQC2018-005-084-PERDF A way of making EuropeMCIN/AEI FPU18/04475 IJC2018-036092-

Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy

The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit these requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack a unified vision of these techniques, and a methodological workflow that supports their usage. Hence, we present the Sherpa.ai Federated Learning framework that is built upon a holistic view of federated learning and differential privacy. It results from both the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases.SHERPA Europe S.L. OTRI-4137Spanish GovernmentEuropean Commission TIN2017-89517-PSpanish Government fellowship programmes Formacion de Profesorado Universitario FPU18/04475 Juan de la Cierva Incorporacion IJC2018-036092-

Educación internacional

Resumen basado en el de la publicaciónSe aborda la Eduación Internacional como disciplina universitaria que reflexiona continuamente sobre las políticas educativas internacionales que interactúan con los escenarios sociales, políticos, económicos y culturales de los países del mundo. Se define cuál es el objeto, modelos que se han construido y bases conceptuales de la Educación Internacional. Se abordan los conceptos que resultan claves para el estudio de la educación desde una perspectiva internacional. Se estudian los procesos de cambio y reforma que afrontan los sistemas educativos. Se identifican los retos educativos futuros y el modo en que los distintos sistemas los afrontan. Se expone el papel que desempeñan los organismos internacionales en el ámbito de la educación.ValenciaBiblioteca de Educación del Ministerio de Educación, Cultura y Deporte; Calle San Agustín 5 -3 Planta; 28014 Madrid; Tel. +34917748000; [email protected]