Stacy Patterson

• PhD
• Professor (Associate) at Rensselaer Polytechnic Institute

We present Poisson Binomial Mechanism Vertical Federated Learning (PBM-VFL), a communication-efficient Vertical Federated Learning algorithm with Differential Privacy guarantees. PBM-VFL combines Secure Multi-Party Computation with the recently introduced Poisson Binomial Mechanism to protect parties' private datasets during model training. We defi...

Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federated learning to create personal...

We propose flexible vertical federated learning (Flex-VFL), a distributed machine algorithm that trains a smooth, nonconvex function in a distributed system with vertically partitioned data. We consider a system with several parties that wish to collaboratively learn a global function. Each party holds a local dataset; the datasets have different f...

We propose LESS-VFL, a communication-efficient feature selection method for distributed systems with vertically partitioned data. We consider a system of a server and several parties with local datasets that share a sample ID space but have different feature sets. The parties wish to collaboratively train a model for a prediction task. As part of t...

Developments in autonomous aircraft, such as electrical vertical take-off and landing (eVTOL) vehicles and multicopter drones, raise safety-critical concerns in populated areas. This article presents the ASSURE ( A nalysis of S afety-Critical S ystems U sing Formal Methods-Based R untime E valuation ) framework, which is a colle...

We propose Flexible Vertical Federated Learning (Flex-VFL), a distributed machine algorithm that trains a smooth, non-convex function in a distributed system with vertically partitioned data. We consider a system with several parties that wish to collaboratively learn a global function. Each party holds a local dataset; the datasets have different...

Successfully attaining consensus in the absence of a centralized coordinator is a fundamental problem in distributed multi-agent systems. We analyze progress in the Synod consensus protocol—which does not assume a unique leader—under the assumptions of asynchronous communication and potential agent failures. We identify a set of sufficient conditio...

One of the intensely studied concepts of network robustness is $r$-robustness, which is a network topology property quantified by an integer $r$. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining $r$-robustness is intractable for large networks. In this paper, we prop...

We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo’s vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TDCD), a com...

Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and computational capabilities, and thus must offload their data so it can be processed to provide actionable int...

We propose Compressed Vertical Federated Learning (C-VFL) for communication-efficient training on vertically partitioned data. In C-VFL, a server and multiple parties collaboratively train a model on their respective features utilizing several local iterations and sharing compressed intermediate results periodically. Our work provides the first the...

Unmanned aerial vehicles (UAVs) are becoming a viable platform for sensing and estimation in a wide variety of applications including disaster response, search and rescue, and security monitoring. These sensing UAVs have limited battery and computational capabilities, and thus must offload their data so it can be processed to provide actionable int...

We consider problems of optimal motion control for multi-agent systems where assignments as well as motions have costs. In particular, we consider a demand distribution and a distribution of resource agents, which require and provide support respectively. We formulate a time-varying assignment problem which trades off two typically competing costs,...

We study diffusion and consensus dynamics in a network of networks model. In this model, there is a collection of subnetworks, connected to one another using a small number of links. We consider a setting where the links between networks have small weights, or are used less frequently than links within each subnetwork. Using spectral perturbation t...

We study second-order consensus dynamics with random additive disturbances. To quantify the robustness of these networks, we investigate three different performance measures: the steady-state variance of pairwise differences between vertex states, the steady-state variance of the deviation of each vertex state from the average, and the total steady...

Autonomous air traffic management (ATM) operations for urban air mobility (UAM) will necessitate the use of distributed protocols for decentralized coordination between aircraft. As UAM operations are time-critical, it will be imperative to have formal guarantees of progress for the distributed protocols used in ATM. Under asynchronous settings, me...

Autonomous air traffic management (ATM) operations for urban air mobility (UAM) will necessitate the use of distributed protocols for decentralized coordination between aircraft. As UAM operations are time-critical, it will be imperative to have formal guarantees of progress for the distributed protocols used in ATM. Under asynchronous settings, me...

We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TDCD), a com...

Successfully attaining consensus in the absence of a centralized coordinator is a fundamental problem in distributed multi-agent systems. We analyze progress in the Synod consensus protocol—which does not assume a unique leader—under the assumptions of asynchronous communication and potential agent failures. We identify a set of sufficient conditio...

Successfully attaining consensus in the absence of a centralized coordinator is a fundamental problem in distributed multi-agent systems. We analyze progress in the Synod consensus protocol -- which does not assume a unique leader -- under the assumptions of asynchronous communication and potential agent failures. We identify a set of sufficient co...

We study the French-DeGroot opinion dynamics in a social network with two polarizing parties. We consider a network in which the leaders of one party are given, and we pose the problem of selecting the leader set of the opposing party so as to shift the average opinion to a desired value. When each party has only one leader, we express the average...

We consider decentralized model training in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TD...

This work presents formal progress envelopes applied to flight systems for distinctly classifying a system's state space into regions where a formal proof of progress for a distributed algorithm holds or does not hold. It also presents an approach for runtime integration of formal methods in the dynamic data-driven applications systems (DDDAS) arch...

In autonomous air-traffic management scenarios of the future, manned and unmanned aircraft will be able to safely navigate through the National Airspace System, independent of centralized air-traffic controllers, by sharing critical data necessary for maintaining standard separation with each other. Under such conditions, every aircraft must have s...

We propose Multi-Level Local SGD, a distributed gradient method for learning a smooth, non-convex objective in a heterogeneous multi-level network. Our network model consists of a set of disjoint sub-networks, with a single hub and multiple worker nodes; further, worker nodes may have different operating rates. The hubs exchange information with on...

Immersive technologies enable Mixed Reality (MR), a form of spatial computing to blur the physical and digital creating a sense or presence. MR is well-positioned to be the next dis-ruptive technology changing the way we work, live, think, and behave. MR over Mobile Edge Computing (MEC)/5G faces two unsolved challenges. The first is how to execute...

We present a framework for scheduling multifunction serverless applications over a hybrid public-private cloud. A set of serverless jobs is input as a batch, and the objective is to schedule function executions over the hybrid platform to minimize the cost of public cloud use, while completing all jobs by a specified deadline. As this scheduling pr...

We present two new consensus algorithms for dynamic networks. The first, Fast Raft, is a variation on the Raft consensus algorithm that reduces the number of message rounds in typical operation. Fast Raft is ideal for fast-paced distributed systems where membership changes over time and where sites must reach consensus quickly. The second, C-Raft,...

We present new operational semantics for serverless computing that model the event-driven relationships between serverless functions, as well as their interaction with platform services such as databases and object stores. These semantics precisely encapsulate how control transfers between functions, both directly and through reads and writes to pl...

We study the problem of optimal network design in a network of networks, a graph composed of a set of disjoint subgraphs, and a set of designed edges between them. Nodes obey noisy consensus dynamics, and our system model allows for both positive and negative edge weights. We quantify system performance by its coherence, an $H_2$ norm that captur...

We present a framework for performance optimization in serverless edge-cloud platforms using dynamic task placement. We focus on applications for smart edge devices, for example, smart cameras or speakers, that need to perform processing tasks on input data in real to near-real time. Our framework allows the user to specify cost and latency require...

We study diffusion and consensus dynamics in a Network of Networks model. In this model, there is a collection of sub-networks, connected to one another using a small number of links. We consider a setting where the links between networks have small weights, or are used less frequently than links within each sub-network. Using spectral perturbation...

We investigate disagreement and polarization in a social network with two polarizing sources of information. First, we define disagreement and polarization indices in two-party leader-follower models of opinion dynamics. We then give expressions for the indices in terms of a graph Laplacian. The expressions show a relationship between these quantit...

We present new operational semantics for serverless computing that model the event-driven relationships between serverless functions, as well as their interaction with platforms services such as databases and object stores. These semantics precisely encapsulate how control transfers between functions, both directly and through reads and writes to p...

We investigate disagreement and polarization in a social network with two polarizing sources of information. First, we define disagreement and polarization indices in two-party leader-follower models of opinion dynamics. We then give expressions for the indices in terms of a graph Laplacian. The expressions show a relationship between these quantit...

We study French-Degroot opinion dynamics in a social network with two polarizing parties. We consider a network in which the leaders of one party are given, and we pose the problem of selecting the leader set of the opposing party so as to shift the average opinion to a desired value. This problem generalizes the intensely studied problem of influe...

We present a novel conflict-aware flight planning approach that avoids the possibility of near mid-air collisions (NMACs) in the flight planning stage. Our algorithm computes a valid flight-plan for an aircraft (ownship) based on a starting time, a set of discrete way-points in 3D space, discrete values of ground speed, and a set of available fligh...

We investigate the performance of m-th order consensus systems with stochastic external perturbations, where a subset of leader nodes incorporates absolute information into their control laws. The system performance is measured by its coherence, an $H_2$ norm that quantifies the total steady-state variance of the deviation from the desired trajecto...

We present a container-based system to automatically run and evaluate networked applications that implement distributed algorithms. Our implementation of this design leverages lightweight, networked Docker containers to provide students with fast, accurate, and helpful feedback about the correctness of their submitted code. We provide a simple, eas...

The emerging trend of edge computing has led several cloud providers to release their own platforms for performing computation at the 'edge' of the network. We compare two such platforms, Amazon AWS Greengrass and Microsoft Azure IoT Edge, using a new benchmark comprising a suite of performance metrics. We also compare the performance of the edge f...

We study the problem of maximizing opinion diversity in a social network that includes opinion leaders with binary opposing opinions. The members of the network who are not leaders form their opinions using the French-DeGroot model of opinion dynamics. To quantify the diversity of such a system, we adapt two diversity measures from ecology to our s...

The vast majority of real-world networks are scale-free, loopy, and sparse, with a power-law degree distribution and a constant average degree. In this paper, we study first-order consensus dynamics in binary scale-free networks, where vertices are subject to white noise. We focus on the coherence of networks characterized in terms of the H <sub xm...

We present BubbleTouch, an open source quasi-static simulator for robotic tactile skins. BubbleTouch can be used to simulate contact with a robot's tactile skin patches as it interacts with humans and objects. The simulator creates detailed traces of contact forces that can be used in experiments in tactile contact activities. We summarize the desi...

We study the problem of maximizing the number of spanning trees in a connected graph with n vertices and m edges, by adding at most k edges from a given set of q candidate edges, a problem that has applications in many domains. We give both algorithmic and hardness results for this problem: 1) We give a greedy algorithm that obtains an approximatio...

We study the problem of maximizing the number of spanning trees in a connected graph by adding at most $k$ edges from a given candidate edge set. We give both algorithmic and hardness results for this problem: - We give a greedy algorithm that, using submodularity, obtains an approximation ratio of $(1 - 1/e - \epsilon)$ in the exponent of the numb...

We present a framework for object recognition with robotic skins with embedded arrays of tactile sensing elements. Our approach is based on theoretical foundations in compressed sensing and compressed learning. In our framework, tactile data is compressed during acquisition, potentially in-hardware, and we perform recognition directly on the compre...

The vast majority of real-world networks are scale-free, loopy, and sparse, with a power-law degree distribution and a constant average degree. In this paper, we study first-order consensus dynamics in binary scale-free networks, where vertices are subject to white noise. We focus on the coherence of networks characterized in terms of the $H_2$-nor...

We consider the leader selection problem in a network with consensus dynamics where both leader and follower agents are subject to stochastic external disturbances. The performance of the system is quantified by the total steady-state variance of the node states, and the goal is to identify the set of leaders that minimizes this variance. We first...

We consider the leader selection problem in a network with consensus dynamics where both leader and follower agents are subject to stochastic external disturbances. The performance of the system is quantified by the total steady-state variance of the node states, and the goal is to identify the set of leaders that minimizes this variance. We first...

In this paper, we study second order consensus dynamics with random additive disturbances. We investigate three different performance measures: the steady-state variance of pairwise differences between vertex states, the steady-state variance of the deviation of each vertex state from the average, and the total steady-state variance of the system....

We study the performance of leader-follower noisy consensus networks and, in particular, the relationship between this performance and the locations of the leader nodes. Two types of dynamics are considered: 1) noise-free leaders, in which leaders dictate the trajectory exactly and followers are subject to external disturbances and 2) noise-corrupt...

We study the performance of leader-follower noisy consensus networks, and in particular, the relationship between this performance and the locations of the leader nodes. Two types of dynamics are considered (1) noise-free leaders, in which leaders dictate the trajectory exactly and followers are subject to external disturbances, and (2) noise-corru...

The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable human-level dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, data acquisition,...

We consider how to connect a set of disjoint networks to optimize the performance of the resulting composite network. We quantify this performance by the coherence of the composite network, which is defined by an H2 norm of the system. Two dynamics are considered: noisy consensus dynamics with and without stubborn agents. For noisy consensus dynami...

The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable human-level dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, power consumption...

We study the problem of optimal leader selection in consensus networks under two performance measures: 1) formation coherence when subject to additive perturbations, as quantified by the steady-state variance of the deviation from the desired trajectory, and 2) convergence rate to a consensus value. The objective is to identify the set of k leaders...

We study the problem of optimal leader selection in consensus networks under two performance measures (1) formation coherence when subject to additive perturbations, as quantified by the steady-state variance of the deviation from the desired trajectory, and (2) convergence rate to a consensus value. The objective is to identify the set of $k$ lead...

We consider the problem of regularized regression in a network of communication-constrained devices. Each node has local data and objectives, and the goal is for the nodes to optimize a global objective. We develop a distributed optimization algorithm that is based on recent work on semi-stochastic proximal gradient methods. Our algorithm employs i...

Whole body tactile perception via tactile skins offers large benefits for robots in unstructured environments. To fully realize this benefit, tactile systems must support real-time data acquisition over a massive number of tactile sensor elements. We present a novel approach for scalable tactile data acquisition using compressed sensing. We first d...

To analyze data distributed across the world, one can use distributed computing power to take advantage of data locality and achieve higher throughput. The multi-cloud model, a composition of multiple clouds, can provide cost-effective computing resources to process such distributed data. As multicolour becomes more and more accessible from cloud u...

We study the problem of optimal leader selection in consensus networks with noisy relative information. The objective is to identify the set of k leaders that minimizes the formation's deviation from the desired trajectory established by the leaders. An optimal leader set can be found by an exhaustive search over all possible leader sets; however,...

Distributed collaborative spectrum sensing has been considered for Cognitive Radio (CR) in order to cope with fading and shadowing effects that affect a single CR performance, without the communication overhead of centralized cooperation through a fusion center. In this paper, we consider collaborative spectrum sensing by a distributed network of C...

We study the problem of optimal leader selection in consensus networks with noisy relative information. The objective is to identify the set of $k$ leaders that minimizes the formation's deviation from the desired trajectory established by the leaders. An optimal leader set can be found by an exhaustive search over all possible leader sets; however...

We study the problem of leader selection in leader-follower multi-agent systems that are subject to stochastic disturbances. This problem arises in applications such as vehicle formation control, distributed clock synchronization, and distributed localization in sensor networks. We pose a new leader selection problem called the in-network leader se...

We consider the problem of in-network compressed sensing, where the goal is to recover a global, sparse signal from local measurements using only local computation and communication. Our approach to this distributed compressed sensing problem is based on the centralized Iterative Hard Thresholding algorithm (IHT). In time-varying networks, the netw...

We address the problem of in-network analytics for data that is generated by sensors at the edge of the network. Specifically, we consider the problem of summarizing a continuous physical phenomenon, such as temperature or pollution, over a geographic region like a road network. Samples are collected by sensors placed alongside roads as well as in...

We consider first and second order consensus algorithms in networks with stochastic disturbances. We quantify the deviation from consensus using the notion of network coherence, which can be expressed as an $H_2$ norm of the stochastic system. We use the setting of fractal networks to investigate the question of whether a purely topological measure...

We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal $x$, and the objective is for the agents to recover $x$ from their collective measurements using only communication with neighbors in the network. Our distributed approach to this problem is based on the centrali...

We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective measurements at a minimal communication cost and with low computational complexity. A naive distribut...

We present Overlapping Cluster Decomposition (OCD), a novel distributed algorithm for network optimization targeted for networks with dynamic demands and link prices. OCD uses a dual decomposition of the global problem into local optimization problems in each node's neighborhood. The local solutions are then reconciled to find the global optimal so...

We present a framework for concurrency control and availability in multi-datacenter datastores. While we consider Google's Megastore as our motivating example, we define general abstractions for key components, making our solution extensible to any system that satisfies the abstraction properties. We first develop and analyze a transaction manageme...

We present a framework for concurrency control and availability in multi-datacenter datastores. While we consider Google's Megastore as our motivating example, we define general abstractions for key components, making our solution extensible to any system that satisfies the abstraction properties. We first develop and analyze a transaction manageme...

We consider distributed consensus and vehicular formation control problems. Specifically we address the question of whether local feedback is sufficient to maintain coherence in large-scale networks subject to stochastic disturbances. We define macroscopic performance measures which are global quantities that capture the notion of coherence; a noti...

We study distributed consensus algorithms in fractal networks where agents are subject to external disturbances. We characterize the coherence of these networks in terms of an H2 norm of the system that captures how closely agents track the consensus value. We show that, in first-order systems, the coherence measure is closely related to the global...

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diffusion over online social networks is a critical resear...

We consider the problem of leader-based distributed coordination in networks where agents are subject to stochastic disturbances, but where certain designated leaders are immune to those disturbances. Specifically, we address the effect of leader selection on the coherence of the network, defined in terms of an H₂ norm of the system. Thi...