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Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. As a result, only a small subset of clients can participate in FL at a given time. It is important to understand how partial client participation affects convergence, but most existing works have either considered idealized participation patterns or obtained results with non-zero optimality error for generic patterns. In this paper, we provide a unified convergence analysis for FL with arbitrary client participation. We first introduce a generalized version of federated averaging (FedAvg) that amplifies parameter updates at an interval of multiple FL rounds. Then, we present a novel analysis that captures the effect of client participation in a single term. By analyzing this term, we obtain convergence upper bounds for a wide range of participation patterns, including both non-stochastic and stochastic cases, which match either the lower bound of stochastic gradient descent (SGD) or the state-of-the-art results in specific settings. We also discuss various insights, recommendations, and experimental results.

Many systems today distribute trust across multiple parties such that the system provides certain security properties if a subset of the parties are honest. In the past few years, we have seen an explosion of academic and industrial cryptographic systems built on distributed trust, including secure multi-party computation applications (e.g., private analytics, secure learning, and private key recovery) and blockchains. These systems have great potential for improving security and privacy, but face a significant hurdle on the path to deployment. We initiate study of the following problem: a single organization is, by definition, a single party, and so how can a single organization build a distributed-trust system where corruptions are independent? We instead consider an alternative formulation of the problem: rather than ensuring that a distributed-trust system is set up correctly by design, what if instead, users can audit a distributed-trust deployment? We propose a framework that enables a developer to efficiently and cheaply set up any distributed-trust system in a publicly auditable way. To do this, we identify two application-independent building blocks that we can use to bootstrap arbitrary distributed-trust applications: secure hardware and an append-only log. We show how to leverage existing implementations of these building blocks to deploy distributed-trust systems, and we give recommendations for infrastructure changes that would make it easier to deploy distributed-trust systems in the future.

In recent years, there have been valuable efforts and contributions to make the process of RDF knowledge graph creation traceable and transparent; extending and applying declarative mapping languages is an example. One challenging step is the traceability of procedures that aim to overcome interoperability issues, a.k.a. data-level integration. In most pipelines, data integration is performed by ad-hoc programs, preventing traceability and reusability. However, formal frameworks provided by function-based declarative mapping languages such as FunUL and RML+FnO empower expressiveness. Data-level integration can be defined as functions and integrated as part of the mappings performing schema-level integration. However, combining functions with the mappings introduces a new source of complexity that can considerably impact the required number of resources and execution time. We tackle the problem of efficiently executing mappings with functions and formalize the transformation of them into function-free mappings. These transformations are the basis of an optimization process that aims to perform an eager evaluation of function-based mapping rules. These techniques are implemented in a framework named Dragoman. We demonstrate the correctness of the transformations while ensuring that the function-free data integration processes are equivalent to the original one. The effectiveness of Dragoman is empirically evaluated in 230 testbeds composed of various types of functions integrated with mapping rules of different complexity. The outcomes suggest that evaluating function-free mapping rules reduces execution time in complex knowledge graph creation pipelines composed of large data sources and multiple types of mapping rules. The savings can be up to 75%, suggesting that eagerly executing functions in mapping rules enable making these pipelines applicable and scalable in real-world settings.

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

The Unsplittable Flow on a Path (UFP) problem has garnered considerable attention as a challenging combinatorial optimization problem with notable practical implications. Steered by its pivotal applications in power engineering, the present work formulates a novel generalization of UFP, wherein demands and capacities in the input instance are monotone step functions over the set of edges. As an initial step towards tackling this generalization, we draw on and extend ideas from prior research to devise a quasi-polynomial time approximation scheme (QPTAS) under the premise that the demands and capacities lie in a quasi-polynomial range. Second, retaining the same assumption, an efficient logarithmic approximation is introduced for the single-source variant of the problem. Finally, we round up the contributions by designing a (kind of) black-box reduction that, under some mild conditions, allows to translate LP-based approximation algorithms for the studied problem into their counterparts for the Alternating Current Optimal Power Flow (AC OPF) problem -- a fundamental workflow in operation and control of power systems.

We study the generation of dependent random numbers in a distributed fashion in order to enable privatized distributed learning by networked agents. We propose a method that we refer to as local graph-homomorphic processing; it relies on the construction of particular noises over the edges to ensure a certain level of differential privacy. We show that the added noise does not affect the performance of the learned model. This is a significant improvement to previous works on differential privacy for distributed algorithms, where the noise was added in a less structured manner without respecting the graph topology and has often led to performance deterioration. We illustrate the theoretical results by considering a linear regression problem over a network of agents.

A new approach to calculating the finite Fourier transform is suggested throughout the process of this study. The idea that the series has been updated with the appropriate modification and purification, which serves as the basis for the study, and that this update functions as the basis for the investigation is the conceptual goal of this method, which was designed especially for the purpose of this study. It is provided here that this methodology, which was designed especially for the purpose of this study, has been updated with the appropriate modification and purification, which serves as the basis for the study, is provided here. This study also used this update as the premise to get started. In order for this approach to be successful, the starting point must be the presumption that the series has been appropriately purified and organized to the point where it can be considered adequate. The attributes of this series were discovered as a result of the work that was ordered to choose an acceptable application of the Fourier series, to apply it, and to conduct an analysis of it in relation to the finite Fourier transform. These qualities were determined this study. The results of this study provided a better understanding of the characteristics of this series.

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology. After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents. Then, we propose a distributed gradient descent procedure to solve the GCN training problem. The resulting model distributes computation along three lines: during inference, during back-propagation, and during optimization. Convergence to stationary solutions of the GCN training problem is also established under mild conditions. Finally, we propose an optimization criterion to design the communication topology between agents in order to match with the graph describing data relationships. A wide set of numerical results validate our proposal. To the best of our knowledge, this is the first work combining graph convolutional neural networks with distributed optimization.

The demand for artificial intelligence has grown significantly over the last decade and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, in order to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges, first and foremost the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.

In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage of the resources available on mobile devices and preserve users' privacy, the idea of mobile distributed machine learning is proposed. It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization of the global model. This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information. Another benefit is the bandwidth reduction, as various kinds of local data can now participate in the training process without being uploaded to the server. In this paper, we provide a comprehensive survey on recent studies of mobile distributed machine learning. We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications.