Personne

Ali H. Sayed

Publications associées (142)

Diffusion Stochastic Optimization for Min-Max Problems

The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(ϵ-2) to achieve an ϵ-stationary solut ...

2025

On the Trade-off between Flatness and Optimization in Distributed Learning

Ali H. Sayed

Institute of Electrical and Electronics Engineers (IEEE)2025

Decentralized Adversarial Training over Graphs

Ali H. Sayed, Elsa Rizk

Institute of Electrical and Electronics Engineers (IEEE)2025

Multi-Agent Reinforcement Learning in Partially Observable Environments Using Social Learning

Ali H. Sayed, Ainur Zhaikhan

This work employs a social learning strategy to estimate the global state in a partially observable multi-agent reinforcement learning (MARL) setting. We prove that the proposed methodology can achieve results within an ε-neighborhood of the solution for a ...

IEEE2025

Social Learning with Dependent Agents

Ali H. Sayed

Social learning strategies implement a distributed decision-making process where several agents, connected over a graph, exchange their beliefs about some hypotheses of interest. Each agent uses some local model that describes the marginal distribution of ...

IEEE2025

Optimal Combination Policies for Error Exponent Maximization in Social Learning

Ali H. Sayed, Mert Kayaalp, Ping Hu

Distributed decision-making over networks involves multiple agents collaborating to achieve a common goal. In the social learning process, where agents aim at inferring an unknown state from a stream of local observations, the probability of error in their ...

IEEE2025

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Ali H. Sayed

To protect both local gradients and estimated parameters in distributed learning, this paper introduces a masked diffusion (MD) strategy, leading to two algorithms: the MD stochastic gradient (MD-SG) and the MD primal-dual stochastic gradient (MPD-SG). The ...

2025

Fundamental Social Learning Scaling Law for Tracking Hidden Markov Models

Ali H. Sayed, Malek Khammassi

This paper studies the problem of interconnected agents collaborating to track a dynamic state from partially informative observations, where the dynamic state evolves according to a slowly varying finite-state Markov chain. Although the centralized versio ...

IEEE2025

Diffusion Learning Over Adaptive Competing Networks

Ali H. Sayed

In this paper, we study a dynamic game between two networks. The networks compete by optimizing two coupled objective functions. Agents within the same network work toward a common goal and are regarded as cooperative agents; they exchange their strategies ...

IEEE2025

Non-asymptotic performance of social machine learning under limited data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifyin ...

2025

Asynchronous Diffusion Learning with Agent Subsampling and Local Updates

Ali H. Sayed, Elsa Rizk

In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each agent independently chooses when to participate throughout the algorithm and t ...

Institute of Electrical and Electronics Engineers Inc.2024

Adaptive Social Learning for Tracking Rare Transition Markov Chains

Ali H. Sayed, Virginia Bordignon, Malek Khammassi

Adaptive Social Learning (ASL) enables consistent truth learning in nonstationary environments. In this framework, agents linked by a graph, exchange their local beliefs with neighbors to track some underlying state of interest. This state can drift over t ...

European Signal Processing Conference, EUSIPCO2024

Distributed Adaptive Learning Under Communication Constraints

Ali H. Sayed

We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. To this end, the agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of infor ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Social Learning in Community Structured Graphs

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

Traditional social learning frameworks consider environments with a homogeneous state, where each agent receives observations conditioned on that true state of nature. In this work, we relax this assumption and study the distributed hypothesis testing prob ...

2024

Diffusion Moving-Average Adaptation Over Networks

Ali H. Sayed

Recently, the diffusion moving-average (D-MA) scheme has been proposed as a way to combat noisy links over adaptive networks. However, the current theoretical results focus on networks with mean-square error costs where the optimal local solution agrees wi ...

2024

Social Learning with Adaptive Models

Ali H. Sayed, Virginia Bordignon

In social learning, a network of agents assigns probability scores (beliefs) to some hypotheses of interest, based on the observation of streaming data. First, each agent updates locally its belief with the information extracted from the current data throu ...

IEEE2024

Socially Intelligent Networks: A framework for decision making over graphs

Ali H. Sayed

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

2024

Causal Influence in Federated Edge Inference

Yunus Inan, Ali H. Sayed, Mert Kayaalp

In this paper, we consider a setting where heterogeneous agents with connectivity are performing inference using unlabeled streaming data. Observed data are only partially informative about the target variable of interest. In order to overcome the uncertai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Graph Exploration for Effective Multiagent Q-Learning

Ali H. Sayed, Ainur Zhaikhan

This article proposes an exploration technique for multiagent reinforcement learning (MARL) with graph-based communication among agents. We assume that the individual rewards received by the agents are independent of the actions by the other agents, while ...

Ieee-Inst Electrical Electronics Engineers Inc2024

On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference

Emre Telatar, Yunus Inan, Ali H. Sayed, Mert Kayaalp

We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing problem under linear and log-linear combination rules. We show that under both combination strategies, agents are able to learn the truth exponentially fast, with ...

Ieee-Inst Electrical Electronics Engineers Inc2024