681 citations · 976 across the 58 of their papers we have counts for
8 papers · 1 filter
FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices
Jiaxin Zhang, Xingwei Wang, Bo Yi +6
Heterogeneous Federated Learning (HFL) aims to train models across devices with diverse resource budgets while preserving data privacy. Existing HFL methods typically bind training…
Principled Direction-Free Intrinsic Motivation through Model-Free Epistemic Free-Energy Estimators
Alireza Furutanpey, Schahram Dustdar
Across environments with mixed sources of uncertainty, unsupervised reinforcement learning requires intrinsic motivation that does not precommit to a particular direction of surpri…
BIPPO: Budget-Aware Independent PPO for Energy-Efficient Federated Learning Services
Anna Lackinger, Andrea Morichetta, Pantelis A. Frangoudis +1
Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL does not natively consider infr…
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication
Alireza Furutanpey, Pantelis A. Frangoudis, Patrik Szabo +1
This paper investigates the adversarial robustness of Deep Neural Networks (DNNs) using Information Bottleneck (IB) objectives for task-oriented communication systems. We empirical…
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
Anastasiya Danilenka, Alireza Furutanpey, Victor Casamayor Pujol +5
Handling heterogeneity and unpredictability are two core problems in pervasive computing. The challenge is to seamlessly integrate devices with varying computational resources in a…
Adaptive Stream Processing on Edge Devices through Active Inference
Boris Sedlak, Victor Casamayor Pujol, Andrea Morichetta +2
The current scenario of IoT is witnessing a constant increase on the volume of data, which is generated in constant stream, calling for novel architectural and logical solutions fo…