5 papers
Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding
Pietro Talli, Federico Mason, Federico Chiariotti +1
Traditional Reinforcement Learning (RL) frameworks generally assume that the agent perceives the state of the underlying Markov process instantaneously and then takes actions accor…
Secure Goal-Oriented Communication: Defending against Eavesdropping Timing Attacks
Federico Mason, Federico Chiariotti, Pietro Talli +1
Goal-oriented Communication (GoC) is a new paradigm that plans data transmission to occur only when it is instrumental for the receiver to achieve a certain goal. This leads to the…
Eavesdropping on Goal-Oriented Communication: Timing Attacks and Countermeasures
Federico Mason, Federico Chiariotti, Pietro Talli +1
Goal-oriented communication is a new paradigm that considers the meaning of transmitted information to optimize communication. One possible application is the remote monitoring of…
To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
Maddalena Boscaro, Federico Mason, Federico Chiariotti +1
Artificial Intelligence (AI) is a key component of 6G networks, as it enables communication and computing services to adapt to end users' requirements and demand patterns. The mana…
Pragmatic Communication for Remote Control of Finite-State Markov Processes
Pietro Talli, Edoardo David Santi, Federico Chiariotti +4
Pragmatic or goal-oriented communication can optimize communication decisions beyond the reliable transmission of data, instead aiming at directly affecting application performance…