Phd Position F/m Robust Federated Learning

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regional economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

**Contexte et atouts du poste**:
The position is part of a new Marie Curie Training Network called FINALITY, in which Inria joins forces with top universities and industries, including IMDEA, KTH, TU Delft, the University of Avignon (Project Leader), the Cyprus Institute, Nokia, Telefonica, Ericsson, Orange, and others. The PhD students will have opportunities for internships with other academic and industry partners and will be able to participate in thematic summer schools and workshops organized by the project.

Only people who have spent less than one year in France in the last 3 years are eligible.

**Mission confiée**:
Federated Learning (FL) empowers a multitude of IoT devices, including mobile phones and sensors, to collaboratively train a global machine learning model while retaining their data locally [1,2]. A prominent example of FL in action is Google's Gboard, which uses a FL-trained model to predict subsequent user inputs on smartphones [3].

Two primary challenges arise during the training phase of FL [4]:
Data Privacy: Ensuring user data remains confidential. Even though the data is kept locally by the devices, it has been shown that an honest-but-curious server can still reconstruct data samples [5,6], sensitive attributes [7,8], and the local model [9] of a targeted device. In addition, the server can conduct membership inference attacks [10] to identify whether a data sample is involved in the training or source inference attacks to determine which device stores a given data sample [11].

Security Against Malicious Participants: Ensuring the learning process is not derailed by harmful actors. Recent research has demonstrated that, in the absence of protective measures, a malicious agent can deteriorate the model performance by simply flipping the labels [12] and/or the sign of the gradient [13] and even inject backdoors into the model [14] (backdoors are hidden vulnerabilities, which can be exploited under certain conditions predefined by the attacker, like some specific inputs).

To enhance system security against adversarial threats, Byzantine resilient mechanisms are implemented on the server side. These algorithms are designed to identify and mitigate potentially detrimental actions or inputs from users, ensuring that even if some components act maliciously or erratically, the overall system remains functional and secure [21,22,23,24]. Experiments [21] reveal that integrating these Byzantine resilient mechanisms sustains neural network accuracy at 90.7%, even when 10% of the agents maliciously flip the labels on the MNIST dataset. In contrast, without such protection, the accuracy of the neural network drops significantly to 77.3%.

Integrating differential privacy with Byzantine resilience presents a notable challenge. Recent research suggests that when these two security measures are combined in their current forms, the effectiveness of the resulting algorithm disproportionately depends on the number of parameters in the machine learning model (_d_) [25]. In particular, it requires either the batch size to grow linearly with the square root of _d_, or the proportion of the malicious agents in the system to decrease with rate inversely proportional to the square root of _d_. For a realistic model such as ResNet-50 (with around 25 million parameters), the batch size should be larger than 5000, which is clearly impractical. To tackle this problem, novel Byzantine resilient algorithms have been recently proposed [26,27]. However, these algorithms encounter significant computational complexity, with a rate of at least _d_3_ in each communication round. Hence, there is a pressing need for innovative methods that can seamlessly integrate differential privacy and Byzantine resilience with low computational complexity to train practical neural networks.

**Project objective**
The goal of this PhD is to