Federated Learning. Yang Liu
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1.3.1 RESEARCH ISSUES IN FEDERATED LEARNING
Federated learning was studied by Google in a research paper published in 2016 on arXiv.1 Since then, it has been an area of active research in the AI community as evidenced by the fast-growing volume of preprints appearing on arXiv. Yang et al. [2019] provide a comprehensive survey of recent advances of federated learning.
Recent research work on federated learning are mainly focused on improving security and statistical challenges [Yang et al., 2019, Mancuso et al., 2019]. Cheng et al. [2019] proposed SecureBoost in the setting of vertical federated learning, which is a novel lossless privacy-preserving tree-boosting system. SecureBoost provides the same level of accuracy as the non-privacy-preserving approach. It is theoretically proven that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that rely on centralized datasets [Cheng et al., 2019].
Liu et al. [2019] presents a flexible federated transfer learning framework that can be effectively adapted to various secure multi-party ML tasks. In this framework, the federation allows knowledge to be shared without compromising user privacy, and enables complimentary knowledge to be transferred in the network via transfer learning. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party.
In a federated learning system, we can assume that participating parties are honest, semi-honest, or malicious. When a party is malicious, it is possible for a model to taint its data in training. The possibility of model poisoning attacks on federated learning initiated by a single non-colluding malicious agent is discussed in Bhagoji et al. [2019]. A number of strategies to carry out model poisoning attack were investigated. It was shown that even a highly constrained adversary can carry out model poisoning attacks while simultaneously maintaining stealth. The work of Bhagoji et al. [2019] reveals the vulnerability of the federated learning settings and advocates the need to develop effective defense strategies.
Re-examining the existing ML models under the federated learning settings has become a new research direction. For example, combining federated learning with reinforcement learning has been studied in Zhuo et al. [2019], where Gaussian differentials on the information shared among agents when updating their local models were applied to protect the privacy of data and models. It has been shown that the proposed federated reinforcement learning model performs close to the baselines that directly take all joint information as input [Zhuo et al., 2019].
Another study in Smith et al. [2017] showed that multi-task learning is naturally suited to handle the statistical challenges of federated learning, where separate but related models are learned simultaneously at each node. The practical issues, such as communication cost, stragglers, and fault tolerance in distributed multi-task learning and federated learning, were considered. A novel systems-aware optimization method was put forward, which achieves significant improved efficiency compared to the alternatives.
Federated learning has also been applied in the fields of computer vision (CV), e.g., medical image analysis [Sheller et al., 2018, Liu et al., 2018, Huang and Liu, 2019], natural language processing (NLP) (see, e.g., Chen et al. [2019]), and recommender systems (RS) (see, e.g., Ammad-ud-din et al. [2019]). This will be further reviewed in Chapter 8.
Regarding applications of federated learning, the researchers at Google have applied federated learning in mobile keyboard prediction [Bonawitz and Eichner et al., 2019, Yang et al., 2018, Hard et al., 2018], which has achieved significant improvement in prediction accuracy without exposing mobile user data. Researchers at Firefox have used federated learning for search word prediction [Hartmann, 2018]. There is also new research effort to make federated learning more personalizable [Smith et al., 2017, Chen et al., 2018].
1.3.2 OPEN-SOURCE PROJECTS
Interest in federated learning is not only limited to theoretical work. Research on the development and deployment of federated learning algorithms and systems is also flourishing. There are several fast-growing open-source projects of federated learning.
• Federated AI Technology Enabler (FATE) [WeBank FATE, 2019] is an open-source project initiated by the AI department of WeBank2 to provide a secure computing framework to support the federated AI ecosystem [WeBank FedAI, 2019]. It implements secure computation protocols based on homomorphic encryption (HE) and secure multi-party computation (MPC). It supports a range of federated learning architectures and secure computation algorithms, including logistic regression, tree-based algorithms, DL (artificial neural networks), and transfer learning. For more information on FATE, readers can refer to the GitHub FATE website [WeBank FATE, 2019] and the FedAI website [WeBank FedAI, 2019].
• TensorFlow3 Federated project [Han, 2019, TFF, 2019, Ingerman and Ostrowski, 2019, Tensorflow-federated, 2019] (TFF) is an open-source framework for experimenting with federated ML and other computations on decentralized datasets. TFF enables developers to simulate existing federated learning algorithms on their models and data, as well as to experiment with novel algorithms. The building blocks provided by TFF can also be used to implement non-learning computations, such as aggregated analytics over decentralized data. The interfaces of TFF are organized in two layers: (1) the federated learning (FL) application programming interface (API) and (2) federated Core (FC) API. TFF enables developers to declaratively express federated computations, so that they can be deployed in diverse runtime environments. Included in TFF is a single-machine simulation run-time for experimentation.
• TensorFlow-Encrypted [TensorFlow-encrypted, 2019] is a Python library built on top of TensorFlow for researchers and practitioners to experiment with privacy-preserving ML. It provides an interface similar to that of TensorFlow, and aims to make the technology readily available without requiring user to be experts in ML, cryptography, distributed systems, and high-performance computing.
• coMind [coMind.org, 2018, coMindOrg, 2019] is an open-source project for training privacy-preserving federated DL models. The key component of coMind is the implementation of the federated averaging algorithm [McMahan et al., 2016a, Yu et al., 2018], which is training ML models in a collaborative way while preserving user privacy and data security. coMind is built on top of TensorFlow and provides high-level APIs for implementing federated learning.
• Horovod [Sergeev and Balso, 2018, Horovod, 2019], developed by Uber, is an open-source distributed training framework for DL. It is based on the open message passing interface (MPI) and works on top of popular DL frameworks, such as TensorFlow and PyTorch.4 The goal of Horovod is to make distributed DL fast and easy to use. Horovod supports federated learning via open MPI and currently, encryption is not yet supported.
• OpenMined/PySyft [Han, 2019, OpenMined, 2019, Ryffel et al., 2018, PySyft, 2019, Ryffel, 2019] provides two methods for privacy preservation: (1) federated learning and (2) differential privacy. OpenMined further supports two methods of secure computation through multi-party computation and homomorphic encryption. OpenMined has made available the PySyft library [PySyft, 2019], which is the first open-source federated learning framework for building secure and scalable ML models [Ryffel, 2019]. PySyft is simply a hooked extension of PyTorch. For users who are familiar with PyTorch, it is very easy to implement federated learning systems with PySyft. Federated learning extension based on the TensorFlow framework is currently being developed within OpenMined.
• LEAF Beanchmark [LEAF, 2019, Caldas et al., 2019], maintained by Carnegie Mellon University and Google AI, is a modular benchmarking framework for ML in federated settings, with applications in federated learning, multi-task learning, meta-learning, and on-device learning. LEAF includes a suite of open-source federated datasets (e.g., FEMNIST, Sentiment140, and Shakespeare), a rigorous evaluation framework, and a set of reference implementations, aiming to capture the reality, obstacles, and intricacies of practical federated learning environments. LEAF enables researchers and practitioners in these domains to investigate new proposed solutions under more realistic assumptions and settings. LEAF will include additional tasks and datasets in its future releases.