arXiv subjects

Alessio Maritan

Publications and source records attributed to Alessio Maritan.

5 recordsLinked to original sources

arXiv2025

Distributed clustering in partially overlapping feature spaces

We introduce and address a novel distributed clustering problem where each participant has a private dataset containing only a subset of all available features, and some features are included in multiple datasets. This scenario occurs in many real-world applications, such as in healthcare, where different institutions have complementary data on similar patients. We propose two different algorithms suitable for solving distributed clustering problems that exhibit this type of feature space heterogeneity. The first is a federated algorithm in which participants collaboratively update a set of global centroids. The second is a one-shot algorithm in which participants share a statistical parametrization of their local clusters with the central server, who generates and merges synthetic proxy datasets. In both cases, participants perform local clustering using algorithms of their choice, which provides flexibility and personalized computational costs. Pretending that local datasets result from splitting and masking an initial centralized dataset, we identify some conditions under which the proposed algorithms are expected to converge to the optimal centralized solution. Finally, we test the practical performance of the algorithms on three public datasets.

cs.DS↗

arXiv2025

StaffPro: an LLM Agent for Joint Staffing and Profiling

Large language model (LLM) agents integrate pre-trained LLMs with modular algorithmic components and have shown remarkable reasoning and decision-making abilities. In this work, we investigate their use for two tightly intertwined challenges in workforce management: staffing, i.e., the assignment and scheduling of tasks to workers, which may require team formation; and profiling, i.e., the continuous estimation of workers' skills, preferences, and other latent attributes from unstructured data. We cast these problems in a formal mathematical framework that links scheduling decisions to latent feature estimation, and we introduce StaffPro, an LLM agent that addresses staffing and profiling jointly. Differently from existing staffing solutions, StaffPro allows expressing optimization objectives using natural language, accepts textual task descriptions and provides high flexibility. StaffPro interacts directly with humans by establishing a continuous human-agent feedback loop, ensuring natural and intuitive use. By analyzing human feedback, our agent continuously estimates the latent features of workers, realizing life-long worker profiling and ensuring optimal staffing performance over time. A consulting firm simulation example demonstrates that StaffPro successfully estimates workers' attributes and generates high quality schedules. With its innovative design, StaffPro offers a robust, interpretable, and human-centric solution for automated personnel management.

cs.AI↗

arXiv2023

FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation

Federated learning is a distributed learning framework that allows a set of clients to collaboratively train a model under the orchestration of a central server, without sharing raw data samples. Although in many practical scenarios the derivatives of the objective function are not available, only few works have considered the federated zeroth-order setting, in which functions can only be accessed through a budgeted number of point evaluations. In this work we focus on convex optimization and design the first federated zeroth-order algorithm to estimate the curvature of the global objective, with the purpose of achieving superlinear convergence. We take an incremental Hessian estimator whose error norm converges linearly, and we adapt it to the federated zeroth-order setting, sampling the random search directions from the Stiefel manifold for improved performance. In particular, both the gradient and Hessian estimators are built at the central server in a communication-efficient and privacy-preserving way by leveraging synchronized pseudo-random number generators. We provide a theoretical analysis of our algorithm, named FedZeN, proving local quadratic convergence with high probability and global linear convergence up to zeroth-order precision. Numerical simulations confirm the superlinear convergence rate and show that our algorithm outperforms the federated zeroth-order methods available in the literature.

cs.LG↗

arXiv2023

Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus

This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimization and information exchange between neighbouring nodes. We introduce a Newton-type fully distributed optimization algorithm, Network-GIANT, which is based on GIANT, a Federated learning algorithm that relies on a centralized parameter server. The Network-GIANT algorithm is designed via a combination of gradient-tracking and a Newton-type iterative algorithm at each node with consensus based averaging of local gradient and Newton updates. We prove that our algorithm guarantees semi-global and exponential convergence to the exact solution over the network assuming strongly convex and smooth loss functions. We provide empirical evidence of the superior convergence performance of Network-GIANT over other state-of-art distributed learning algorithms such as Network-DANE and Newton-Raphson Consensus.

math.OC↗

arXiv2023

ZO-JADE: Zeroth-order Curvature-Aware Multi-Agent Convex Optimization

In this work we address the problem of convex optimization in a multi-agent setting where the objective is to minimize the mean of local cost functions whose derivatives are not available (e.g. black-box models). Moreover agents can only communicate with local neighbors according to a connected network topology. Zeroth-order (ZO) optimization has recently gained increasing attention in federated learning and multi-agent scenarios exploiting finite-difference approximations of the gradient using from $2$ (directional gradient) to $2d$ (central difference full gradient) evaluations of the cost functions, where $d$ is the dimension of the problem. The contribution of this work is to extend ZO distributed optimization by estimating the curvature of the local cost functions via finite-difference approximations. In particular, we propose a novel algorithm named ZO-JADE, that by adding just one extra point, i.e. $2d+1$ in total, allows to simultaneously estimate the gradient and the diagonal of the local Hessian, which are then combined via average tracking consensus to obtain an approximated Jacobi descent. Guarantees of semi-global exponential stability are established via separation of time-scales. Extensive numerical experiments on real-world data confirm the efficiency and superiority of our algorithm with respect to several other distributed zeroth-order methods available in the literature based on only gradient estimates.

math.OC↗