Search Results for author:
Found 6 papers, 4 papers with code
Orthogonal Calibration for Asynchronous Federated Learning
In this paper, we propose OrthoFL, an orthogonal calibration framework that decouples global and local learning progress and adjusts global shifts to minimize interference before merging them into local models.
SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions
Natural language interaction with sensing systems is crucial for enabling all users to comprehend sensor data and its impact on their everyday lives.
SensorQA: A Question Answering Benchmark for Daily-Life Monitoring
To address this gap, we introduce SensorQA, the first human-created question-answering (QA) dataset for long-term time-series sensor data for daily life monitoring.
Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing
On-device learning has emerged as a prevailing trend that avoids the slow response time and costly communication of cloud-based learning.
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level.
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge
To address the challenges, we propose Self-Supervised ContrAstive Lifelong LEarning without Prior Knowledge (SCALE) which can extract and memorize representations on the fly purely from the data continuum.
