{"id":842272,"date":"2022-05-16T09:00:00","date_gmt":"2022-05-16T16:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=842272"},"modified":"2022-08-17T09:05:39","modified_gmt":"2022-08-17T16:05:39","slug":"flute-a-scalable-federated-learning-simulation-platform","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/flute-a-scalable-federated-learning-simulation-platform\/","title":{"rendered":"FLUTE: A scalable federated learning simulation platform"},"content":{"rendered":"\n
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Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the amount of research already conducted, it\u2019s difficult to implement due to its many moving parts\u2014a significant deviation from traditional ML pipelines.<\/p>\n\n\n\n

The challenges in working with federated learning result from the diversity of local data and end-node hardware, privacy concerns, and optimization constraints. These are compounded by the sheer volume of federated learning clients and their data and necessitates a wide skill set, significant interdisciplinary research efforts, and major engineering resources to manage.\u202fIn addition, federated learning applications often need to scale the learning process to millions of clients to simulate a real-world environment. All of these challenges underscore the need for a simulation platform, one that enables researchers and developers to perform proof-of-concept implementations and validate performance before building and deploying their ML models.\u202f<\/p>\n\n\n\n

A versatile framework for federated learning<\/h2>\n\n\n\n

Today, the Privacy in AI<\/a> team at Microsoft Research is thrilled to introduce Federated Learning Utilities and Tools for Experimentation (opens in new tab)<\/span><\/a> (FLUTE) as a framework for running large-scale offline federated learning simulations, which we discuss in detail in the paper, \u201cFLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations<\/a>.\u201d In creating FLUTE, our goal was to develop a high-performance simulation platform that enables quick prototyping of federated learning research and makes it easier to implement federated learning applications.<\/p>\n\n\n\n

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Read the paper<\/a><\/div>\n\n\n\n
Download software<\/a><\/div>\n<\/div>\n\n\n\n
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There has been a lot of research in the last few years directed at tackling the many challenges in working with federated learning, including setting up learning environments, providing privacy guarantees, implementing model-client updates, and lowering communication costs. FLUTE addresses many of these while providing enhanced customization and enabling new research on a realistic scale. It also allows developers and researchers to test and experiment with certain scenarios, such as data privacy, communication strategies, and scalability, before implementing their ML model in a production framework.<\/p>\n\n\n\n

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One of FLUTE\u2019s main benefits is its native integration with\u202fAzure ML workspaces, leveraging the platform\u2019s features to manage and track experiments, parameter sweeps, and model snapshots. Its distributed nature is based on Python and PyTorch, and the flexibly designed client-server architecture helps researchers and developers quickly prototype novel approaches to federated learning. However, FLUTE\u2019s key innovation and technological differentiator is the ease it provides in implementing new scenarios for experimentation in core areas of active research in a robust high-performance simulator.\u202f<\/p>\n\n\n\n

FLUTE offers a platform where all clients are implemented as isolated object instances, as shown in Figure 1. The interface between the server and the remaining workers relies on messages that contain client IDs and training information, with MPI (opens in new tab)<\/span><\/a> as the main communication protocol. Local data on each client stays within local storage boundaries and is never aggregated with other local sources. Clients only communicate gradients to the central server.<\/p>\n\n\n\n

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Figure 1: FLUTE\u2019s client-server architecture and workflow. First, the server pushes the initial global model to the clients and sends training information. Then, the clients train their instances of the global model with locally available data. Finally, all clients return the information to the server to aggregate the pseudo-gradients and produce a new global model that will be updated to the clients. This three-step process repeats for all rounds of training.<\/figcaption><\/figure>\n\n\n\n

The following features contribute to FLUTE\u2019s versatile framework and enable experimentation with new federated learning approaches:\u202f<\/p>\n\n\n\n