2 citations · 3 across the 6 of their papers we have counts for
7 papers · 1 filter
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication
Hideya Ochiai, Riku Nishihata, Eisuke Tomiyama +2
Anomaly detection is an important function in IoT applications for finding outliers caused by abnormal events. Anomaly detection sometimes comes with high-frequency data sampling w…
Remembering Transformer for Continual Learning
Yuwei Sun, Ippei Fujisawa, Arthur Juliani +2
Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task learning interferes with previously learned knowledge. Existing data f…
Associative Transformer
Yuwei Sun, Hideya Ochiai, Zhirong Wu +2
Emerging from the pairwise attention in conventional Transformers, there is a growing interest in sparse attention mechanisms that align more closely with localized, contextual lea…
Meta Neural Coordination
Yuwei Sun
Meta-learning aims to develop algorithms that can learn from other learning algorithms to adapt to new and changing environments. This requires a model of how other learning algori…
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks
Naoya Tezuka, Hideya Ochiai, Yuwei Sun +1
Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to con…
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis
Yuwei Sun, Hideya Ochiai, Jun Sakuma
Model poisoning attacks on federated learning (FL) intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such comprom…