A Federated Learning Domain-Specific Language Based on FastFlow for Edge and IoT Environments

Design and implement a domain-specific language for federated learning workflows targeting edge and IoT environments, using FastFlow as the underlying parallel programming framework.

The candidate will: 1. Review federated learning programming models and DSL design principles. 2. Study FastFlow. 3. Define the syntax and semantics of the proposed DSL. 4. Implement a compiler, interpreter, or translation layer. 5. Integrate the DSL with a federated learning runtime. 6. Evaluate expressiveness, overhead, portability, and execution performance.

6 to 9 months according to the student's background.

• Knowledge of C++ and Python
• Knowledge of parallel programming
• Basic knowledge of federated learning and edge computing

1. Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medic, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, & Marco Aldinucci (2023). Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning. In Proceedings of the 20th ACM International Conference on Computing Frontiers, CF 2023, Bologna, Italy, May 9-11, 2023 (pp. 73–83). ACM.