Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus, protecting the clients' privacy becomes a primary concern. For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices. Despite several efforts on this topic, no work has explicitly addressed the challenges of federated learning in semantic segmentation for driving so far. To fill this gap, we propose FedDrive, a new benchmark consisting of three settings and two datasets, incorporating the real-world challenges of statistical heterogeneity and domain generalization. We benchmark state-of-the-art algorithms from the federated learning literature through an in-depth analysis, combining them with style transfer methods to improve their generalization ability. We demonstrate that correctly handling normalization statistics is crucial to deal with the aforementioned challenges. Furthermore, style transfer improves performance when dealing with significant appearance shifts. We plan to make both the code and the benchmark publicly available to the research community.

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving / Fantauzzo, Lidia; Fani', Eros; Caldarola, Debora; Tavera, Antonio; Cermelli, Fabio; Ciccone, Marco; Caputo, Barbara. - (2022). ((Intervento presentato al convegno 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) tenutosi a Kyoto (JPN) nel 23-27 Ottobre 2022.

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Fantauzzo,Lidia;Fani',Eros;Caldarola,Debora;Tavera,Antonio;Cermelli,Fabio;Ciccone,Marco;Caputo,Barbara
2022

Abstract

Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus, protecting the clients' privacy becomes a primary concern. For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices. Despite several efforts on this topic, no work has explicitly addressed the challenges of federated learning in semantic segmentation for driving so far. To fill this gap, we propose FedDrive, a new benchmark consisting of three settings and two datasets, incorporating the real-world challenges of statistical heterogeneity and domain generalization. We benchmark state-of-the-art algorithms from the federated learning literature through an in-depth analysis, combining them with style transfer methods to improve their generalization ability. We demonstrate that correctly handling normalization statistics is crucial to deal with the aforementioned challenges. Furthermore, style transfer improves performance when dealing with significant appearance shifts. We plan to make both the code and the benchmark publicly available to the research community.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2970566