Andrea Goldsmith
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View article: Spectral Graph Clustering under Differential Privacy: Balancing Privacy, Accuracy, and Efficiency
Spectral Graph Clustering under Differential Privacy: Balancing Privacy, Accuracy, and Efficiency Open
We study the problem of spectral graph clustering under edge differential privacy (DP). Specifically, we develop three mechanisms: (i) graph perturbation via randomized edge flipping combined with adjacency matrix shuffling, which enforces…
View article: Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking
Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking Open
Watermarking has become a key technique for proprietary language models, enabling the distinction between AI-generated and human-written text. However, in many real-world scenarios, LLM-generated content may undergo post-generation edits, …
View article: On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models Open
We investigate privacy-preserving spectral clustering for community detection within stochastic block models (SBMs). Specifically, we focus on edge differential privacy (DP) and propose private algorithms for community recovery. Our work e…
View article: PCAST: Report to the President on Improving Groundwater Security in the United States
PCAST: Report to the President on Improving Groundwater Security in the United States Open
https://www.whitehouse.gov/wp-content/uploads/2024/12/PCAST-Report-on-GW_14DEC2024_Final-1.pdf The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged w…
View article: Collaborative Inference over Wireless Channels with Feature Differential Privacy
Collaborative Inference over Wireless Channels with Feature Differential Privacy Open
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically involves a three-stage…
View article: Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks
Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks Open
We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently. We adopt a semi-decentralized setup, wher…
View article: Over-the-Air Collaborative Inference with Feature Differential Privacy
Over-the-Air Collaborative Inference with Feature Differential Privacy Open
Collaborative inference in next-generation networks can enhance Artificial Intelligence (AI) applications, including autonomous driving, personal identification, and activity classification. This method involves a three-stage process: a) d…
View article: Compressing Large Language Models using Low Rank and Low Precision Decomposition
Compressing Large Language Models using Low Rank and Low Precision Decomposition Open
The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces $\rm CALDERA$ -- a new post-training LLM compression algorithm that harnesses the inheren…
View article: Differentially Private Online Community Detection for Censored Block Models: Algorithms and Fundamental Limits
Differentially Private Online Community Detection for Censored Block Models: Algorithms and Fundamental Limits Open
We study the private online change detection problem for dynamic communities, using a censored block model (CBM). We consider edge differential privacy (DP) in both local and central settings, and propose joint change detection and communi…
View article: How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems
How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems Open
Multi-agent cyberphysical systems enable new capabilities in efficiency, resilience, and security. The unique characteristics of these systems prompt a reevaluation of their security concepts, including their vulnerabilities, and mechanism…
View article: Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots (evolved version)
Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots (evolved version) Open
We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision m…
View article: Women in Networks
Women in Networks Open
Andrea Goldsmith is Dean of the School of Engineering and Applied Science and the Arthur LeGrand Doty Professor of Electrical and Computer Engineering at Princeton University. During her career she has significantly advanced the state-of-t…
View article: Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy Open
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a ce…
View article: Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems
Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems Open
This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its …
View article: Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots
Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots Open
We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision m…
View article: Composite IG/FTR Channel Performance in Wireless Communication Systems
Composite IG/FTR Channel Performance in Wireless Communication Systems Open
We present a composite wireless fading model encompassing
\nmultipath fading and shadowing based on fluctuating
\ntwo-ray (FTR) fading and inverse gamma (IG) shadowing.
\nWe first determine an alternative framework for the statistical
\nch…
View article: Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams Open
When humans collaborate with each other, they often make decisions by observing others and considering the consequences that their actions may have on the entire team, instead of greedily doing what is best for just themselves. We would li…
View article: Semi-Decentralized Federated Learning with Collaborative Relaying
Semi-Decentralized Federated Learning with Collaborative Relaying Open
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every communication round to the PS, each client computes a local co…
View article: Semi-Decentralized Federated Learning with Collaborative Relaying
Semi-Decentralized Federated Learning with Collaborative Relaying Open
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every communication round to the PS, each client computes a local co…
View article: Composite IG/FTR Channel Performance in Wireless Communication Systems
Composite IG/FTR Channel Performance in Wireless Communication Systems Open
We present a composite wireless fading model encompassing multipath fading and shadowing based on fluctuating two-ray (FTR) fading and inverse gamma (IG) shadowing. We first determine an alternative framework for the statistical characteri…
View article: Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying Open
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks. The lack of constant connectivity induces a large generalization gap, especially when the local data distributio…
View article: Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms
Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms Open
High-dimensional models often have a large memory footprint and must be quantized after training before being deployed on resource-constrained edge devices for inference tasks. In this work, we develop an information-theoretic framework fo…
View article: Learned Factor Graphs for Inference From Stationary Time Sequences
Learned Factor Graphs for Inference From Stationary Time Sequences Open
The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been de…
View article: A Tree Search Approach for Maximum-Likelihood Decoding of Reed-Muller Codes
A Tree Search Approach for Maximum-Likelihood Decoding of Reed-Muller Codes Open
A low-complexity tree search approach is presented that achieves the maximum-likelihood (ML) decoding performance of Reed-Muller (RM) codes. The proposed approach generates a bit-flipping tree that is traversed to find the ML decoding resu…
View article: Interference Reduction in Virtual Cell Optimization
Interference Reduction in Virtual Cell Optimization Open
Virtual cell optimization clusters cells into neighborhoods and performs optimized resource allocation over each neighborhood. In prior works we proposed resource allocation schemes to mitigate the interference caused by transmissions in t…
View article: Successive Syndrome-Check Decoding of Polar Codes
Successive Syndrome-Check Decoding of Polar Codes Open
A two-part successive syndrome-check decoding of polar codes is proposed with the first part successively refining the received codeword and the second part checking its syndrome. A new formulation of the successive-cancellation (SC) decod…
View article: Decentralized Optimization Over Noisy, Rate-Constrained Networks: Achieving Consensus by Communicating Differences
Decentralized Optimization Over Noisy, Rate-Constrained Networks: Achieving Consensus by Communicating Differences Open
In decentralized optimization, multiple nodes in a network collaborate to minimize the sum of their local loss functions. The information exchange between nodes required for this task, is often limited by network connectivity. We consider …
View article: Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks
Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks Open
We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors collaborate locally within predefined sensor clusters and fuse their noisy se…
View article: Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams Open
When humans collaborate with each other, they often make decisions by observing others and considering the consequences that their actions may have on the entire team, instead of greedily doing what is best for just themselves. We would li…
View article: Alternative Formulations for the Fluctuating Two-Ray Fading Model
Alternative Formulations for the Fluctuating Two-Ray Fading Model Open
We present two alternative formulations for the distribution of the fluctuating two-ray (FTR) fading model, which simplify its statistical characterization and subsequent use for performance evaluation. New expressions for the probability …