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Found 5 papers, 2 papers with code
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models
In this work, we propose a device-cloud collaborative controlled learning framework, called DC-CCL, enabling a cloud-side large vision model that cannot be directly deployed on the mobile device to still benefit from the device-side local samples.
On-Device Model Fine-Tuning with Label Correction in Recommender Systems
To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices.
Federated Submodel Optimization for Hot and Cold Data Features
We theoretically proved the convergence rate of FedSubAvg by deriving an upper bound under a new metric called the element-wise gradient norm.
Distributed Optimization over Block-Cyclic Data
We consider practical data characteristics underlying federated learning, where unbalanced and non-i. i. d.
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process.
