Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing
Mobile sensing appears as a promising solution for health inference problem (eg, influenza-
like symptom recognition) by leveraging diverse smart sensors to capture fine-grained …
like symptom recognition) by leveraging diverse smart sensors to capture fine-grained …
Evaluation framework for large-scale federated learning
Federated learning is proposed as a machine learning setting to enable distributed edge
devices, such as mobile phones, to collaboratively learn a shared prediction model while …
devices, such as mobile phones, to collaboratively learn a shared prediction model while …
Cross-device federated learning for mobile health diagnostics: A first study on COVID-19 detection
Federated learning (FL) aided health diagnostic models can incorporate data from a large
number of personal edge devices (eg, mobile phones) while keeping the data local to the …
number of personal edge devices (eg, mobile phones) while keeping the data local to the …
MDLdroid: A ChainSGD-reduce approach to mobile deep learning for personal mobile sensing
Personal mobile sensing is fast permeating our daily lives to enable activity monitoring,
healthcare and rehabilitation. Combined with deep learning, these applications have …
healthcare and rehabilitation. Combined with deep learning, these applications have …
Optimizing federated learning on non-iid data with reinforcement learning
The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …
Fed2kd: Heterogeneous federated learning for pandemic risk assessment via two-way knowledge distillation
The world has suffered a lot from the COVID-19 pandemic. Though vaccines have been
developed, we still need to be ready for its variants and other possible pandemics in the …
developed, we still need to be ready for its variants and other possible pandemics in the …
Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data
Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm,
drawing tremendous attention in both academia and industry. A unique characteristic of FL …
drawing tremendous attention in both academia and industry. A unique characteristic of FL …
Tailorfl: Dual-personalized federated learning under system and data heterogeneity
Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared
model without exposing their raw data. However, heterogeneous devices usually have …
model without exposing their raw data. However, heterogeneous devices usually have …
Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
Recent advancements in deep neural networks (DNN) enabled various mobile deep
learning applications. However, it is technically challenging to locally train a DNN model due …
learning applications. However, it is technically challenging to locally train a DNN model due …
Intelligent Client Selection for Federated Learning using Cellular Automata
Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and
latency minimization in various real-world applications, such as transportation …
latency minimization in various real-world applications, such as transportation …