31 citations · 141 across the 20 of their papers we have counts for
8 papers · 1 filter
Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)
Bahareh Najafi, Saeedeh Parsaeefard, Alberto Leon-Garcia
GNNs have been proven to perform highly effective in various node-level, edge-level, and graph-level prediction tasks in several domains. Existing approaches mainly focus on static…
A Deep Reinforcement Learning Approach for Fair Traffic Signal Control
Majid Raeis, Alberto Leon-Garcia
Traffic signal control is one of the most effective methods of traffic management in urban areas. In recent years, traffic control methods based on deep reinforcement learning (DRL…
Robust Federated Learning by Mixture of Experts
Saeedeh Parsaeefard, Sayed Ehsan Etesami, Alberto Leon Garcia
We present a novel weighted average model based on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated loc…
Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service
Majid Raeis, Ali Tizghadam, Alberto Leon-Garcia
End-to-end delay is a critical attribute of quality of service (QoS) in application domains such as cloud computing and computer networks. This metric is particularly important in…
Estimation of Missing Data in Intelligent Transportation System
Bahareh Najafi, Saeedeh Parsaeefard, Alberto Leon-Garcia
Missing data is a challenge in many applications, including intelligent transportation systems (ITS). In this paper, we study traffic speed and travel time estimations in ITS, wher…
On the Robustness of Cooperative Multi-Agent Reinforcement Learning
Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang +2
In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively take actions as a team to maximize a total team reward. We analyze the robustness of c-MAR…
Representation of Federated Learning via Worst-Case Robust Optimization Theory
Saeedeh Parsaeefard, Iman Tabrizian, Alberto Leon Garcia
Federated learning (FL) is a distributed learning approach where a set of end-user devices participate in the learning process by acting on their isolated local data sets. Here, we…
Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure
Saeedeh Parsaeefard, Iman Tabrizian, Alberto Leon-Garcia
This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking an…