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Found 12 papers, 4 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.
One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with heterogeneous images from a small subset of classes to be recognized.
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.
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration.
On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems
Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to each individual user's local data distribution.
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.
Toward Understanding the Influence of Individual Clients in Federated Learning
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server.
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.
Online Pricing with Reserve Price Constraint for Personal Data Markets
The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.
Secure Federated Submodel Learning
Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning.
From Server-Based to Client-Based Machine Learning: A Comprehensive Survey
Another benefit is the bandwidth reduction because various kinds of local data can be involved in the training process without being uploaded.
