While the quality of machine learning services largely relies on the volume of training data, data regulations such as the General Data Protection Regulation (GDPR) impose stringent requirements on data transfer. Federated learning has emerged as a popular approach for enabling collaborative machine learning without sharing raw data. To facilitate the rapid development of federated learning, efficient and user-friendly federated learning systems are essential. Despite many existing federated learning systems designed for deep learning, tree-based federated learning systems have not been well exploited. This paper presents a tree-based federated learning system under a histogram-sharing scheme, named FedTree, that supports both horizontal and vertical federated training of GBDTs with configurable privacy protection techniques. Our extensive experiments show that FedTree achieves competitive accuracy to centralized training while incurring much less computational cost than the other generic federated learning systems.

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to cross-device FL for millions or billions of devices presents many risks, including performance loss, developer inertia, poor user experience, and unexpected application failures. In addition, the corresponding infrastructure, development costs, and return on investment are difficult to estimate. In this paper, we present a device-cloud collaborative FL platform that integrates with an existing machine learning platform, providing tools to measure real-world constraints, assess infrastructure capabilities, evaluate model training performance, and estimate system resource requirements to responsibly bring FL into production. We also present a decision workflow that leverages the FL-integrated platform to comprehensively evaluate the trade-offs of cross-device FL and share our empirical evaluations of business-critical machine learning applications that impact hundreds of millions of users.

Hyperparameter tuning is critical to the success of federated learning applications. Unfortunately, appropriately selecting hyperparameters is challenging in federated networks, as issues of scale, privacy, and heterogeneity introduce noise in the tuning process and make it difficult to faithfully evaluate the performance of various hyperparameters. In this work we perform the first systematic study on the effect of noisy evaluation in federated hyperparameter tuning. We first identify and rigorously explore key sources of noise, including client subsampling, data and systems heterogeneity, and data privacy. Surprisingly, our results indicate that even small amounts of noise can have a significant impact on tuning methods—reducing the performance of state-of-the-art approaches to that of naive baselines. To address noisy evaluation in such scenarios, we propose a simple and effective approach that leverages public proxy data to boost evaluation signal. Our work establishes general challenges, baselines, and best practices for future work in federated hyperparameter tuning.

Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communication overhead of FL makes training challenging when edge devices have limited network bandwidth. Existing work to optimize FL bandwidth overlooks downstream transmission and does not account for FL client sampling. In this paper we propose GlueFL, a framework that incorporates new client sampling and model compression algorithms to mitigate low download bandwidths of FL clients. GlueFL prioritizes recently used clients and bounds the number of changed positions in compression masks in each round. Across three popular FL datasets and three state-of-the-art strategies, GlueFL reduces downstream client bandwidth by 27% on average and reduces training time by 29% on average.