Project Florida: Federated Learning Made Easy

• URL: http://arxiv.org/abs/2307.11899v1
• Date: Fri, 21 Jul 2023 20:56:20 GMT
• Title: Project Florida: Federated Learning Made Easy
• Authors: Daniel Madrigal Diaz, Andre Manoel, Jialei Chen, Nalin Singal, Robert Sim
• Abstract summary: We present Project Florida, a system architecture and software development kit (SDK) enabling deployment of large-scale Federated Learning (FL) solutions. FL is an approach to machine learning based on a strong data sovereignty principle, i.e., that privacy and security of data is best enabled by storing it at its origin. Project Florida aims to simplify the task of deploying cross-device FL solutions by providing cloud-hosted infrastructure and accompanying task management interfaces.
• Score: 4.829821142951709
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Project Florida, a system architecture and software development kit (SDK) enabling deployment of large-scale Federated Learning (FL) solutions across a heterogeneous device ecosystem. Federated learning is an approach to machine learning based on a strong data sovereignty principle, i.e., that privacy and security of data is best enabled by storing it at its origin, whether on end-user devices or in segregated cloud storage silos. Federated learning enables model training across devices and silos while the training data remains within its security boundary, by distributing a model snapshot to a client running inside the boundary, running client code to update the model, and then aggregating updated snapshots across many clients in a central orchestrator. Deploying a FL solution requires implementation of complex privacy and security mechanisms as well as scalable orchestration infrastructure. Scale and performance is a paramount concern, as the model training process benefits from full participation of many client devices, which may have a wide variety of performance characteristics. Project Florida aims to simplify the task of deploying cross-device FL solutions by providing cloud-hosted infrastructure and accompanying task management interfaces, as well as a multi-platform SDK supporting most major programming languages including C++, Java, and Python, enabling FL training across a wide range of operating system (OS) and hardware specifications. The architecture decouples service management from the FL workflow, enabling a cloud service provider to deliver FL-as-a-service (FLaaS) to ML engineers and application developers. We present an overview of Florida, including a description of the architecture, sample code, and illustrative experiments demonstrating system capabilities.

Related papers

• Federated Learning in Practice: Reflections and Projections [17.445826363802997]
  Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Production systems from organizations like Google, Apple, and Meta demonstrate the real-world applicability of FL. We propose a redefined FL framework that prioritizes privacy principles rather than rigid definitions.
  arXiv  Detail & Related papers  (2024-10-11T15:10:38Z)
• A Survey on Efficient Federated Learning Methods for Foundation Model Training [62.473245910234304]
  Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications.
  arXiv  Detail & Related papers  (2024-01-09T10:22:23Z)
• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights [52.024964564408]
  This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack. It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
  arXiv  Detail & Related papers  (2023-12-07T20:39:57Z)
• FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems [61.335229621081346]
  Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities.
  arXiv  Detail & Related papers  (2023-06-08T13:11:20Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments [4.075095403704456]
  We introduce TorchFL, a performant library for bootstrapping federated learning experiments. TorchFL is built on a bottom-up design using PyTorch and Lightning. Being built on a bottom-up design using PyTorch and Lightning, TorchFL provides ready-to-use abstractions for models, datasets, and FL algorithms.
  arXiv  Detail & Related papers  (2022-11-01T20:31:55Z)
• NVIDIA FLARE: Federated Learning from Simulation to Real-World [11.490933081543787]
  We created NVIDIA FLARE as an open-source development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches.
  arXiv  Detail & Related papers  (2022-10-24T14:30:50Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• FedLess: Secure and Scalable Federated Learning Using Serverless Computing [1.141832715860866]
  Federated Learning (FL) enables remote clients to learn a shared ML model while keeping the data local. We present a novel system and framework for serverless FL, called FedLess. Our system supports multiple commercial and self-hosted F providers and can be deployed in the cloud, on-premise in institutional data centers, and on edge devices.
  arXiv  Detail & Related papers  (2021-11-05T11:14:07Z)
• FLaaS: Federated Learning as a Service [3.128267020893596]
  We present Federated Learning as a Service (FL), a system enabling different scenarios of 3rd-party application collaborative model building. As a proof of concept, we implement it on a mobile phone setting and discuss practical implications of results on simulated and real devices. We demonstrate FL's feasibility in building unique or joint FL models across applications for image object detection in a few hours, across 100 devices.
  arXiv  Detail & Related papers  (2020-11-18T15:56:22Z)
• Wireless Communications for Collaborative Federated Learning [160.82696473996566]
  Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models. Google's seminal FL algorithm requires all devices to be directly connected with a central controller. This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
  arXiv  Detail & Related papers  (2020-06-03T20:00:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.