Federated learning (FL) is an emerging promising paradigm of privacy-preserving machine learning (ML). An important type of FL is cross-silo FL, which enables a small scale of organizations to cooperatively train a shared model by keeping confidential data locally and aggregating weights on a central parameter server. However, the central server may be vulnerable to malicious attacks or software failures in practice. To address this issue, in this paper, we propose DeFL, a novel decentralized weight aggregation framework for cross-silo FL. DeFL eliminates the central server by aggregating weights on each participating node and weights of only the current training round are maintained and synchronized among all nodes. We use Multi-Krum to enable aggregating correct weights from honest nodes and use HotStuff to ensure the consistency of the training round number and weights among all nodes. Besides, we theoretically analyze the Byzantine fault tolerance, convergence, and complexity of DeFL. We conduct extensive experiments over two widely-adopted public datasets, i.e. CIFAR-10 and Sentiment140, to evaluate the performance of DeFL. Results show that DeFL defends against common threat models with minimal accuracy loss, and achieves up to 100x reduction in storage overhead and up to 12x reduction in network overhead, compared to state-of-the-art decentralized FL approaches.

In aircraft industry, market needs evolve quickly in a high competitiveness context. Thisrequires adapting a given aircraft model in minimum time considering for example an increase of range or of the number of passengers such as the A330 family in [1]. In our case study, variants concern the maximum takeoff weight of a given aircraft model. Depending on the configuration, the computation of loads and stress, as defined in [13, 12], to resize the airframe is on the critical path of this aircraft variant definition: this is a time consuming (approximately a year for a new aircraft variant) and costly process, one of the reason being the high dimensionality and the large amount of data. Big Data approaches such as defined by [11] is mandatory to improve the speed, the data value extraction and the responsiveness of the overall process. This study has been realized during aproof of value sprint project within Airbus to demonstrate the usefulness of statistics and machine learning approaches in the Engineering field. In a previous internal project, it has been shown that the family of regression trees [5] works well to predict loads for different aircraft missions in an interpolation context. Thus, we can formulate our problem in this way: is it possible to use dimensional reduction and regression trees-based algorithms to predict loads in an extrapolation context (i.e outside the design space of a certain weight variant) toimprove the actual process?