Federated Learning (FL) has gained prominence as a decentralized and privacy-preserving paradigm that enables multiple clients to collaboratively train a machine learning model under the supervision of a central server. Instead of centralizing the data, clients keep their data locally and share only model parameters during multiple communication rounds. However, recent attacks, such as gradient reconstruction attacks (GRAs) show privacy issues when an attacker knows the communication of a client. In the literature, these privacy issues are mainly explored when clients compute new parameters using a single gradient descent step on their data (FedSGD) and then send them back to the remote server. In a more realistic scenario, the clients' protocol is based on several gradient descent steps (FedAvg). This protocol adds intermediate computation steps, which are unknown from the attacker, thus making GRAs less successful. In this incremental paper, we conduct exhaustive experiments on four state-of-the-art attacks under the FedAvg protocol, on a very basic and a more complex neural network (ResNet-18) with CIFAR100 dataset. These experiments provide the following results 1) a privacy-utility trade-off analysis, 2) insights on the choice of attacks' hyperparameters, 3) the client's local learning rate has little impact on attacks' effectiveness 4) a proof that the privacy risk is not necessarily decreasing over rounds, contrary to common belief.

Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without sharing their local data. The centrality of this framework represents a point of failure which is addressed in literature by blockchain-based federated learning approaches. While ensuring a fully-decentralized solution with traceability, such approaches still face several challenges about integrity, confidentiality and scalability to be practically deployed. In this paper we propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art.

MOTIVATION: We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data. RESULTS: dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. AVAILABILITY AND IMPLEMENTATION: dnadna is a Python (≥3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.