Kiyoshi Nakayama is a former Research Scientist at NEC Laboratories America, Inc.

Posts

BAFFLE: Decentralized Blockchain based Aggregator-Free Federated Learning

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A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational constraints. In this paper, we introduce BAFFLE, an aggregator free, blockchain driven, FL environment that is inherently decentralized. BAFFLE leverages Smart Contracts (SC) to coordinate the round delineation, model aggregation and update tasks in FL. BAFFLE boosts computational performance by decomposing the global parameter space into distinct chunks followed by a score and bid strategy. In order to characterize the performance of BAFFLE, we conduct experiments on a private Ethereum network and use the centralized and aggregator driven methods as our benchmark. We show that BAFFLE significantly reduces the gas costs for FL on the blockchain as compared to a direct adaptation of the aggregator based method. Our results also show that BAFFLE achieves high scalability and computational efficiency while delivering similar accuracy as the benchmark methods.

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Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants

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Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node’s interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.

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Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach

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Cyber-physical systems (CPS) are ubiquitous in several critical infrastructure applications. Forecasting the state of CPS, is essential for better planning, resource allocation and minimizing operational costs. It is imperative to forecast the state of a CPS multiple steps into the future to afford enough time for planning of CPS operation to minimize costs and component wear. Forecasting system state also serves as a precursor to detecting process anomalies and faults. Concomitantly, sensors used for data collection are commodity hardware and experience frequent failures resulting in periods with sparse or no data. In such cases, re-construction through imputation of the missing data sequences is imperative to alleviate data sparsity and enable better performance of down-stream analytic models. In this paper, we tackle the problem of CPS state forecasting and data imputation and characterize the performance of a wide array of deep learning architectures – unidirectional gated and non-gated recurrent architectures, sequence to sequence (Seq2Seq) architectures as well as bidirectional architectures – with a specific focus towards applications in CPS. We also study the impact of procedures like scheduled sampling and attention, on model training. Our results indicate that Seq2Seq models are superior to traditional step ahead forecasting models and yield an improvement of at least 28.5% for gated recurrent architectures and about 87.6% for non-gated architectures in terms of forecasting performance. We also notice that bidirectional models learn good representations for forecasting as well as for data imputation. Bidirectional Seq2Seq models show an average improvement of 17.6% in forecasting performance over their unidirectional counterparts. We also demonstrate the effect of employing an attention mechanism in the context of Seq2Seq architectures and find that it provides an average improvement of 57.12% in the case of unidirectional Seq2Seq architectures while causing a performance decline in the case of bidirectional Seq2Seq architectures. Finally, we also find that scheduled sampling helps in training better models that yield significantly lower forecasting error.

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VeCharge: Intelligent Energy Management for Electric Vehicle charging

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2018’s 1.2 million North American charging ports will grow ten times to over 12.6 million by 2027, according to Navigant, which could overwhelm the nation’s grids. DC Fast charging requires grid upgrade to supply the new charging demand. However, since the utilization ratio of those charging station is currently low. Demand charge cost can reach up to 90% of the total bill. Combining fast charging with energy storage can mitigate grid impacts and reduce demand charges. EV specific pricing is proposed for EV charging by many energy suppliers. Without managed charging, EV owner will lose the benefit of lowering charging cost by avoiding peak hour charging or missing the period when renewable energy generation is abundant.

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Decentralized Transactive Energy Auctions with Bandit Learning

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The power systems worldwide have been embracing the rapid growth of distributed energy resources. Commonly, distributed energy resources exist in the distribution level, such as electric vehicles, rooftop photovoltaic panels, and home battery systems, which cannot be controlled by a centralized entity like a utility. However, a large number of distributed energy resources have potential to reshape the power generation landscape when the owners (prosumers) are allowed to send electricity back to the grids. Transactive energy paradigms are emerging for orchestrating the coordination of prosumers and consumers by enabling the exchange of energy among them. In this paper, we propose a transactive energy auction framework based on blockchain technology for creating trustworthy and transparent transactive environments in distribution networks, which does not rely on a centralized entity to clear transactions. Moreover, we propose intelligent decentralized decision-making strategies by bandit learning for market participants to locally decide their energy prices in auctions. The bandit learning approach can provide market participants with more benefits under the blockchain framework than trading energy with the centralized entity, which is further supported by the preliminary simulated results conducted over our blockchain-based platform.

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Transactive Energy Management with Blockchain Smart Contracts for P2P Multi-Settlement Markets

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Integration of renewables and energy storage, leading to rise of prosumers, has created localized bidirectional flows. As the result, the utility demand has decreased and traditional centralized controller can no longer realize the optimal performance of ever growing distribution systems. To achieve scalable control, exploiting the potential of smart loads and Distributed Energy Resource (DER) controllability, a framework for decentralized Peer-To-Peer (P2P) energy management has been developed to manage localized micro-energy markets. Such decentralized management approach could, in theory, sustain diverse prosumer and utility business models. We have been developing an autonomous decentralized management solution that maximizes the benefit of prosumers while protecting utility assets. This P2P energy trading market leverages Blockchain technology and its Smart Contract framework. This paper presents 1) transactive energy market for P2P multi-settlement markets, 2) architecture of blockchain-based energy management system, 3) smart contract design that solves an economic dispatch problem of DERs to maximize the profit of pro/consumers.

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Demand Charge and Response with Energy Storage

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Commercial and industry (C& I) customers incur two types of electricity charges on their bills: one for the amount of energy usage and another one for the maximum demand during certain billing periods. The second charge type is known as Demand Charge (DC), which could account for over half of a customers’ electricity bill. Those C& I customers often sign up for Demand Response (DR) programs to contribute to peak demand reduction as well as to receive incentives and rewards from participating in the programs. The critical factor of achieving both DR and DC reduction is to recognize the nature of these two types of problems and create an effective strategy that can handle them at the same time by which the benefits from DR incentives and DC reduction are maximized. This paper discusses the possible DR scenarios with DC reduction framework for C& I customers who use a Behind-the-Meter (BTM) energy storage and proposes a consistent real-time procedure of deciding battery’s charging and discharging set points to solve the problem of maximizing the rewards by conducting DRs as well as the savings by reducing DC costs.

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illiad: InteLLigent Invariant and Anomaly Detection in Cyber-Physical Systems

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Cyber-physical systems (CPSs) are today ubiquitous in urban environments. Such systems now serve as the backbone to numerous critical infrastructure applications, from smart grids to IoT installations. Scalable and seamless operation of such CPSs requires sophisticated tools for monitoring the time series progression of the system, dynamically tracking relationships, and issuing alerts about anomalies to operators. We present an online monitoring system (illiad) that models the state of the CPS as a function of its relationships between constituent components, using a combination of model-based and data-driven strategies. In addition to accurate inference for state estimation and anomaly tracking, illiad also exploits the underlying network structure of the CPS (wired or wireless) for state estimation purposes. We demonstrate the application of illiad to two diverse settings: a wireless sensor motes application and an IEEE 33-bus microgrid.

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