Personalized Graph Federated Learning with Differential Privacy

This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every individual device. The proposed approach exploits similarities among different models to provide a more relevant experience for each device, even in situations with diverse data distributions and disproportionate datasets. Furthermore, to ensure a secure and efficient approach to collaborative personalized learning, we study a variant of the PGFL implementation that utilizes differential privacy, specifically zero-concentrated differential privacy, where a noise sequence perturbs model exchanges. Our mathematical analysis shows that the proposed privacy-preserving PGFL algorithm converges to the optimal cluster-specific solution for each cluster in linear time. It also shows that exploiting similarities among clusters leads to an alternative output whose distance to the original solution is bounded, and that this bound can be adjusted by modifying the algorithm's hyperparameters. Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy. Finally, the performance of the proposed PGFL algorithm is examined by performing numerical experiments in the context of regression and classification using synthetic data and the MNIST dataset

A class of diffusion proportionate subband adaptive filters for sparse system identification over distributed networks

This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalized subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variations, namely diffusion proportionate normalized subband adaptive filter (DPNSAF), diffusion μ-law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms

Essays on information, technology and information worker productivity

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2007.Includes bibliographical references.I examine how information technology (IT) skills and use, communication network structures, and the distribution and flow of information in organizations impact individual information worker productivity. The work is divided into three essays based on the task level practices of information workers at a midsize executive recruiting firm: Essay 1: "Information, Technology and Information Worker Productivity: Task Level Evidence." I develop and econometrically test a multistage model of production and interaction activities at the firm, and analyze correlations among network structure, characteristics of information flow and real economic output. I find that (a) IT use is positively correlated with non-linear drivers of productivity; (b) the structure and size of workers' communication networks are highly correlated with performance; (c) an inverted-U shaped relationship exists between multitasking and productivity such that, beyond an optimum, more multitasking is associated with declining project completion rates and revenue generation; and (d) asynchronous information seeking such as email and database use promotes multitasking while synchronous information seeking over the phone shows a negative correlation. These data demonstrate a strong correspondence among technology use, social networks, and productivity for project-based information workers.(cont.) Essay 2: "Network Structure and Information Advantage: Structural Determinants of Access to Novel Information and their Performance Implications." I examine relationships between social network structure, information structure, and individual performance. I build and validate a Vector Space Model of information diversity, develop hypotheses linking two key aspects of network structure - size and diversity - to the distribution of novel information among actors, and test the theory using data on email communication patterns, message content and performance. Results indicate that access to diverse, novel information is related to network structure in non-linear ways, and that network diversity contributes to performance even when controlling for the positive performance effects of access to novel information, suggesting additional benefits to network diversity beyond those conferred through information advantage.(cont.) Essay 3: "Organizational Information Dynamics: Drivers of Information Diffusion in Organizations." I examine drivers of the diffusion of different types of information through organizations by observing several thousand diffusion processes of two types of information -'event news' and 'discussion topics' - from their original first use to their varied recipients over time. I then test the effects of network structure and functional and demographic characteristics of dyadic relationships on the likelihood of receiving each type of information and receiving it sooner. Discussion topics exhibit more shallow diffusion characterized by 'back-and-forth' conversation and are more likely to diffuse vertically up and down the organizational hierarchy, across relationships with a prior working history, and across stronger ties; while news, characterized by a spike in communication and rapid, pervasive diffusion through the organization, is more likely to diffuse laterally as well as vertically, and without regard to the strength or function of relationships. The findings highlight the importance of simultaneous considerations of structure and content in information diffusion studies.by Sinan Aral.Ph.D