106 citations · 333 across the 19 of their papers we have counts for
15 papers · 1 filter
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
Chi Ian Tang, Lorena Qendro, Dimitris Spathis +3
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature…
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh +3
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field. Self-supervised learning (SSL) is a promising approac…
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning
Chi Ian Tang, Lorena Qendro, Dimitris Spathis +3
Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks when trained offline. However, in a Continual Learning (CL) scenario where new data is intr…
Enhancing Efficiency in Multidevice Federated Learning through Data Selection
Fan Mo, Mohammad Malekzadeh, Soumyajit Chatterjee +2
Ubiquitous wearable and mobile devices provide access to a diverse set of data. However, the mobility demand for our devices naturally imposes constraints on their computational an…
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
Ekdeep Singh Lubana, Chi Ian Tang, Fahim Kawsar +2
Federated learning is generally used in tasks where labels are readily available (e.g., next word prediction). Relaxing this constraint requires design of unsupervised learning tec…
FLAME: Federated Learning Across Multi-device Environments
Hyunsung Cho, Akhil Mathur, Fahim Kawsar
Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL…
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
Yash Jain, Chi Ian Tang, Chulhong Min +2
A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is…
Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows
Wiebke Toussaint, Aaron Yi Ding, Fahim Kawsar +1
Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data. On-devi…
FRuDA: Framework for Distributed Adversarial Domain Adaptation
Shaoduo Gan, Akhil Mathur, Anton Isopoussu +3
Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is…
SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices
Chulhong Min, Akhil Mathur, Utku Gunay Acer +2
We present SensiX++ - a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e.g., a camera, a microphone, or IoT sensors. SensiX++ operates on…
On-device Federated Learning with Flower
Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusmão +6
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do…
A first look into the carbon footprint of federated learning
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques +6
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.…
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data
Chongyang Wang, Yuan Gao, Akhil Mathur +3
Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states. Existing automatic protec…
Can Federated Learning Save The Planet?
Xinchi Qiu, Titouan Parcollet, Daniel J. Beutel +3
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.…
Flower: A Friendly Federated Learning Research Framework
Daniel J. Beutel, Taner Topal, Akhil Mathur +8
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, t…