90 citations · 183 across the 9 of their papers we have counts for
9 papers
Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals
Running Zhao, Jiangtao Yu, Hang Zhao +1
Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, cons…
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
Yun-Hin Chan, Rui Zhou, Running Zhao +2
Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handl…
HealthPrism: A Visual Analytics System for Exploring Children's Physical and Mental Health Profiles with Multimodal Data
Zhihan Jiang, Handi Chen, Rui Zhou +6
The correlation between children's personal and family characteristics (e.g., demographics and socioeconomic status) and their physical and mental health status has been extensivel…
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
Yun-Hin Chan, Zhihan Jiang, Jing Deng +1
Federated learning (FL) facilitates edge devices to cooperatively train a global shared model while maintaining the training data locally and privately. However, a common assumptio…
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
Shenghui Li, Edith C. -H. Ngai, Thiemo Voigt
Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants may upload arbitrary local updates to…
Exploiting Features and Logits in Heterogeneous Federated Learning
Yun-Hin Chan, Edith C. -H. Ngai
Due to the rapid growth of IoT and artificial intelligence, deploying neural networks on IoT devices is becoming increasingly crucial for edge intelligence. Federated learning (FL)…
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Shenghui Li, Edith Ngai, Fanghua Ye +3
Federated learning (FL) facilitates distributed training across different IoT and edge devices, safeguarding the privacy of their data. The inherent distributed structure of FL int…
FedHe: Heterogeneous Models and Communication-Efficient Federated Learning
Chan Yun Hin, Ngai Edith
Federated learning (FL) is able to manage edge devices to cooperatively train a model while maintaining the training data local and private. One common assumption in FL is that all…
Auto-weighted Robust Federated Learning with Corrupted Data Sources
Shenghui Li, Edith Ngai, Fanghua Ye +1
Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants while keeping their…