FLaaS: Federated Learning as a Service
Federated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on user devices, avoiding user data to be transferred to centralized servers, and can be enhanced with differential privacy mechanisms. Although FL has been recently deployed in real systems, the possibility of collaborative modeling across different 3rd-party applications has not yet been explored. In this paper, we tackle this problem and present Federated Learning as a Service (FLaaS), a system enabling different scenarios of 3rd-party application collaborative model building and addressing the consequent challenges of permission and privacy management, usability, and hierarchical model training. FLaaS can be deployed in different operational environments. As a proof of concept, we implement it on a mobile phone setting and discuss practical implications of results on simulated and real devices with respect to on-device training CPU cost, memory footprint and power consumed per FL model round. Therefore, we demonstrate FLaaS's feasibility in building unique or joint FL models across applications for image object detection in a few hours, across 100 devices.
Code (0)
등록된 구현이 없습니다.
Tasks
CPUFederated LearningManagementobject-detectionObject DetectionPrivacy PreservingSimilar Papers 제목 키워드 기반
Designing Sustainable Federated Learning as a Service using Neural Architecture Search
The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments. These constraints often lead to infeasible consumer participation and…
Neural Architecture SearchFederated LearningDID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities
We have entered the era of big data, and it is considered to be the "fuel" for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns ab…
Federated LearningManagementNebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing
With advancements in AI infrastructure and Trusted Execution Environment (TEE) technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) is promising to break through the resource constraints…
Federated LearningSchedulingRBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of serv…
Federated LearningDPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
Federated learning (FL) has emerged as a prevalent distributed machine learning scheme that enables collaborative model training without aggregating raw data. Cloud service providers further embrace Federated Learning as…
FairnessFederated LearningScheduling