27 citations · 40 across the 8 of their papers we have counts for
5 papers · 1 filter
Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?
Obaidullah Zaland, Feras M. Awaysheh, Sawsan Al Zubi +2
Federated learning (FL) has emerged as a transformative paradigm for edge intelligence, enabling collaborative model training while preserving data privacy across distributed perso…
Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges
Obaidullah Zaland, Chanh Nguyen, Florian T. Pokorny +1
Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad…
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping
Obaidullah Zaland, Erik Elmroth, Monowar Bhuyan
Federated Learning (FL) is a promising machine learning paradigm that enables participating devices to train privacy-preserved and collaborative models. FL has proven its benefits…
One-Shot Federated Learning with Classifier-Free Diffusion Models
Obaidullah Zaland, Shutong Jin, Florian T. Pokorny +1
Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between clien…
Personalized Multi-tier Federated Learning
Sourasekhar Banerjee, Ali Dadras, Alp Yurtsever +1
The key challenge of personalized federated learning (PerFL) is to capture the statistical heterogeneity properties of data with inexpensive communications and gain customized perf…