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Emanuele Natale

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17 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Fast and Robust Information Spreading in the Noisy PULL Model

Niccolò D'archivio
Amos Korman
Emanuele Natale
Robin Vacus

Efficient information spreading in stochastic multi-agent systems is a core challenge when communication is noisy, bandwidth-limited, and agents lack global coordination. Yet biological systems—such as antcoloniesandfishschools—routinelyovercometheseconstraints: a small number of informed individuals can reliably guide large, uncoordinated populations using minimal, noisy signals. Motivated by these observations, we investigate how reliable information dissemination can be achieved in bio-inspired stochastic settings with limited communication and no global control. We analyze the noisy PULL(ℎ) model, covering a general setting that spans from rumor spreading to majority consensus: a subset of source agents hold initial preferences, and the goal is to converge to the majority preference. Agents passively observe noisy messages from ℎ randomly sampled peers per round. Prior work shows that convergence requires Ω(𝑛/ℎ) rounds even under favorable conditions. We ask: how far can one push simplicity—no synchronization and minimal message size—without compromising convergence speed? We present a quasi self-stabilizing protocol using only 2-bit messages that converges from arbitrary initial states despite severe noise and asynchrony. It achieves optimal convergence time 𝑂((𝑛/ℎ) log𝑛) with high probability, and𝑂(log𝑛) time in the fully connected case ℎ = 𝑛. A key subroutine is an even simpler 1-bit protocolassumingsimultaneousstart, basedonanaturaltwo-phase “listen-then-amplify” mechanism reminiscent of biological strategies. Together, our results connect biologically inspired heuristics with provable guarantees for robust, efficient information dissemination in highly unreliable and uncoordinated systems.

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AAMAS Conference 2025 Conference Paper

Trading-off Accuracy and Communication Cost in Federated Learning

Mattia Jacopo Villani
Emanuele Natale
Frederik Mallmann-Trenn

Leveraging the training-by-pruning paradigm introduced by Zhou et al. [NeurIPS’19], Isik et al. [ICLR’23] introduced a federated learning protocol that achieves a 34-fold reduction in communication cost. We achieve a compression improvements of orders of orders of magnitude over the state-of-the-art. The central idea of our framework is to encode the network weights ® 𝑤 by a the vector of trainable parameters ® 𝑝, such that ® 𝑤 = 𝑄 · ® 𝑝 where 𝑄 is a carefully-generate sparse random matrix (that remains fixed throughout training). In such framework, the previous work of Zhou et al. [NeurIPS’19] is retrieved when 𝑄 is diagonal and ® 𝑝 has the same dimension of ® 𝑤. We instead show that ® 𝑝 can effectively be chosen much smaller than ® 𝑤, while retaining the same accuracy at the price of a decrease of the sparsity of 𝑄. Since server and clients only need to share ® 𝑝, such a trade-off leads to a substantial improvement in communication cost. Moreover, we provide theoretical insight into our framework and establish a novel link between training-by-sampling and random convex geometry.

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NeurIPS Conference 2024 Conference Paper

On the Sparsity of the Strong Lottery Ticket Hypothesis

Emanuele Natale
Davide Ferré
Giordano Giambartolomei
Frédéric Giroire
Frederik Mallmann-Trenn

Considerable research efforts have recently been made to show that a random neural network $N$ contains subnetworks capable of accurately approximating any given neural network that is sufficiently smaller than $N$, without any training. This line of research, known as the Strong Lottery Ticket Hypothesis (SLTH), was originally motivated by the weaker Lottery Ticket Hypothesis, which states that a sufficiently large random neural network $N$ contains sparse subnetworks that can be trained efficiently to achieve performance comparable to that of training the entire network $N$. Despite its original motivation, results on the SLTH have so far not provided any guarantee on the size of subnetworks. Such limitation is due to the nature of the main technical tool leveraged by these results, the Random Subset Sum (RSS) Problem. Informally, the RSS Problem asks how large a random i. i. d. sample $\Omega$ should be so that we are able to approximate any number in $[-1, 1]$, up to an error of $ \epsilon$, as the sum of a suitable subset of $\Omega$. We provide the first proof of the SLTH in classical settings, such as dense and equivariant networks, with guarantees on the sparsity of the subnetworks. Central to our results, is the proof of an essentially tight bound on the Random Fixed-Size Subset Sum Problem (RFSS), a variant of the RSS Problem in which we only ask for subsets of a given size, which is of independent interest.

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AAAI Conference 2022 Conference Paper

Planning with Biological Neurons and Synapses

Francesco d'Amore
Daniel Mitropolsky
Pierluigi Crescenzi
Emanuele Natale
Christos H. Papadimitriou

We revisit the planning problem in the blocks world, and we implement a known heuristic for this task. Importantly, our implementation is biologically plausible, in the sense that it is carried out exclusively through the spiking of neurons. Even though much has been accomplished in the blocks world over the past five decades, we believe that this is the first algorithm of its kind. The input is a sequence of symbols encoding an initial set of block stacks as well as a target set, and the output is a sequence of motion commands such as “put the top block in stack 1 on the table”. The program is written in the Assembly Calculus, a recently proposed computational framework meant to model computation in the brain by bridging the gap between neural activity and cognitive function. Its elementary objects are assemblies of neurons (stable sets of neurons whose simultaneous firing signifies that the subject is thinking of an object, concept, word, etc.), its commands include project and merge, and its execution model is based on widely accepted tenets of neuroscience. A program in this framework essentially sets up a dynamical system of neurons and synapses that eventually, with high probability, accomplishes the task. The purpose of this work is to establish empirically that reasonably large programs in the Assembly Calculus can execute correctly and reliably; and that rather realistic — if idealized — higher cognitive functions, such as planning in the blocks world, can be implemented successfully by such programs.

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ICLR Conference 2022 Conference Paper

Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks

Arthur da Cunha 0001
Emanuele Natale
Laurent Viennot

The lottery ticket hypothesis states that a randomly-initialized neural network contains a small subnetwork which, when trained in isolation, can compete with the performance of the original network. Recent theoretical works proved an even stronger version: every sufficiently overparameterized (dense) neural network contains a subnetwork that, even without training, achieves accuracy comparable to that of the trained large network. These works left as an open problem to extend the result to convolutional neural networks (CNNs). In this work we provide such generalization by showing that, with high probability, it is possible to approximate any CNN by pruning a random CNN whose size is larger by a logarithmic factor.

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TCS Journal 2021 Journal Article

Parallel Load Balancing on constrained client-server topologies

Andrea Clementi
Emanuele Natale
Isabella Ziccardi

We study parallel Load Balancing protocols for the client-server distributed model defined as follows. There is a set C of n clients and a set S of n servers where each client has (at most) a constant number d ⩾ 1 of requests that must be assigned to some server. The client set and the server one are connected to each other via a fixed bipartite graph: the requests of client v can only be sent to the servers in its neighborhood N ( v ). The goal is to assign every client request so as to minimize the maximum load of the servers. In this setting, efficient parallel protocols are available only for dense topologies. In particular, a simple protocol, named raes, has been recently introduced by Becchetti et al. [1] for regular dense bipartite graphs. They show that this symmetric, non-adaptive protocol achieves constant maximum load with parallel completion time O ( log ⁡ n ) and overall work O ( n ), w. h. p. Motivated by proximity constraints arising in some client-server systems, we analyze raes over almost-regular bipartite graphs where nodes may have neighborhoods of small size. In detail, we prove that, w. h. p. , the raes protocol keeps the same performances as above (in terms of maximum load, completion time, and work complexity, respectively) on any almost-regular bipartite graph with degree Ω ( log 2 ⁡ n ). Our analysis significantly departs from that in [1] since it requires to cope with non-trivial stochastic-dependence issues on the random choices of the algorithmic process which are due to the worst-case, sparse topology of the underlying graph.

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SODA Conference 2020 Conference Paper

Finding a Bounded-Degree Expander Inside a Dense One

Luca Becchetti
Andrea Clementi
Emanuele Natale
Francesco Pasquale
Luca Trevisan 0001

It follows from the Marcus-Spielman-Srivastava proof of the Kadison-Singer conjecture that if G = ( V, E ) is a Δ-regular dense expander then there is an edge-induced subgraph H = ( V, E h ) of G of constant maximum degree which is also an expander. As with other consequences of the MSS theorem, it is not clear how one would explicitly construct such a subgraph. We show that such a subgraph (although with quantitatively weaker expansion and near-regularity properties than those predicted by MSS) can be constructed with high probability in linear time, via a simple algorithm. Our algorithm allows a distributed implementation that runs in O (log n ) rounds and does O ( n ) total work with high probability. The analysis of the algorithm is complicated by the complex dependencies that arise between edges and between choices made in different rounds. We sidestep these difficulties by following the combinatorial approach of counting the number of possible random choices of the algorithm which lead to failure. We do so by a compression argument showing that such random choices can be encoded with a non-trivial compression. Our algorithm bears some similarity to the way agents construct a communication graph in a peer-to-peer network, and, in the bipartite case, to the way agents select servers in blockchain protocols.

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AAAI Conference 2019 Conference Paper

Distributed Community Detection via Metastability of the 2-Choices Dynamics

Emilio Cruciani
Emanuele Natale
Giacomo Scornavacca

We investigate the behavior of a simple majority dynamics on networks of agents whose interaction topology exhibits a community structure. By leveraging recent advancements in the analysis of dynamics, we prove that, when the states of the nodes are randomly initialized, the system rapidly and stably converges to a configuration in which the communities maintain internal consensus on different states. This is the first analytical result on the behavior of dynamics for nonconsensus problems on non-complete topologies, based on the first symmetry-breaking analysis in such setting. Our result has several implications in different contexts in which dynamics are adopted for computational and biological modeling purposes. In the context of Label Propagation Algorithms, a class of widely used heuristics for community detection, it represents the first theoretical result on the behavior of a distributed label propagation algorithm with quasi-linear message complexity. In the context of evolutionary biology, dynamics such as the Moran process have been used to model the spread of mutations in genetic populations (Lieberman, Hauert, and Nowak 2005); our result shows that, when the probability of adoption of a given mutation by a node of the evolutionary graph depends super-linearly on the frequency of the mutation in the neighborhood of the node and the underlying evolutionary graph exhibits a community structure, there is a non-negligible probability for species differentiation to occur.

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MFCS Conference 2018 Conference Paper

A Tight Analysis of the Parallel Undecided-State Dynamics with Two Colors

Andrea Clementi
Mohsen Ghaffari 0001
Luciano Gualà
Emanuele Natale
Francesco Pasquale
Giacomo Scornavacca

The Undecided-State Dynamics is a well-known protocol for distributed consensus. We analyze it in the parallel PULL communication model on the complete graph with n nodes for the binary case (every node can either support one of two possible colors, or be in the undecided state). An interesting open question is whether this dynamics is an efficient Self-Stabilizing protocol, namely, starting from an arbitrary initial configuration, it reaches consensus quickly (i. e. , within a polylogarithmic number of rounds). Previous work in this setting only considers initial color configurations with no undecided nodes and a large bias (i. e. , Theta(n)) towards the majority color. In this paper we present an unconditional analysis of the Undecided-State Dynamics that answers to the above question in the affirmative. We prove that, starting from any initial configuration, the process reaches a monochromatic configuration within O(log n) rounds, with high probability. This bound turns out to be tight. Our analysis also shows that, if the initial configuration has bias Omega(sqrt(n log n)), then the dynamics converges toward the initial majority color, with high probability.

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AAMAS Conference 2018 Conference Paper

Phase Transition of the 2-Choices Dynamics on Core-Periphery Networks

Emilio Cruciani
Emanuele Natale
Andr� Nusser
Giacomo Scornavacca

Consider the following process on a network: Each agent initially holds either opinion blue or red; then, in each round, each agent looks at two random neighbors and, if the two have the same opinion, the agent adopts it. This process is known as the 2-Choices dynamics and is arguably the most basic non-trivial opinion dynamics modeling voting behavior on social networks. Despite its apparent simplicity, 2-Choices has been analytically characterized only on networks with a strong expansion property – under assumptions on the initial configuration that establish it as a fast majority consensus protocol. In this work, we aim at contributing to the understanding of the 2-Choices dynamics by considering its behavior on a class of networks with core-periphery structure, a well-known topological assumption in social networks. In a nutshell, assume that a denselyconnected subset of agents, the core, holds a different opinion from the rest of the network, the periphery. Then, depending on the strength of the cut between the core and the periphery, a phasetransition phenomenon occurs: Either the core’s opinion rapidly spreads among the rest of the network, or a metastability phase takes place, in which both opinions coexist in the network for superpolynomial time. The interest of our result is twofold. On the one hand, by looking at the 2-Choices dynamics as a simplistic model of competition among opinions in social networks, our theorem sheds light on the influence of the core on the rest of the network, as a function of the core’s connectivity towards the latter. On the other hand, to the best of our knowledge, we provide the first analytical result which shows a heterogeneous behavior of a simple dynamics as a function of structural parameters of the network. Finally, we validate our theoretical predictions with extensive experiments on real networks.

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AAMAS Conference 2018 Conference Paper

Pooling or Sampling: Collective Dynamics for Electrical Flow Estimation

Luca Becchetti
Vincenzo Bonifaci
Emanuele Natale

The computation of electrical flows is a crucial primitive for many recently proposed optimization algorithms on weighted networks. While typically implemented as a centralized subroutine, the ability to perform this task in a fully decentralized way is implicit in a number of biological systems. Thus, a natural question is whether this task can provably be accomplished in an efficient way by a network of agents executing a simple protocol. We provide a positive answer, proposing two distributed approaches to electrical flow computation on a weighted network: a deterministic process mimicking Jacobi’s iterative method for solving linear systems, and a randomized token diffusion process, based on revisiting a classical random walk process on a graph with an absorbing node. We show that both processes converge to a solution of Kirchhoff’s node potential equations, derive bounds on their convergence rates in terms of the weights of the network, and analyze their time and message complexity.

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SODA Conference 2017 Conference Paper

Find Your Place: Simple Distributed Algorithms for Community Detection

Luca Becchetti
Andrea Clementi
Emanuele Natale
Francesco Pasquale
Luca Trevisan 0001

Given an underlying graph, we consider the following dynamics: Initially, each node locally chooses a value in {-1, 1}, uniformly at random and independently of other nodes. Then, in each consecutive round, every node updates its local value to the average of the values held by its neighbors, at the same time applying an elementary, local clustering rule that only depends on the current and the previous values held by the node. We prove that the process resulting from this dynamics produces a clustering that exactly or approximately (depending on the graph) reflects the underlying cut in logarithmic time, under various graph models that exhibit a sparse balanced cut, including the stochastic block model. We also prove that a natural extension of this dynamics performs community detection on a regularized version of the stochastic block model with multiple communities. Rather surprisingly, our results provide rigorous evidence for the ability of an extremely simple and natural dynamics to address a computational problem that is non-trivial even in a centralized setting. Distributed Algorithms, Averaging Dynamics, Community Detection, Spectral Analysis, Stochastic Block Models.

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SODA Conference 2017 Conference Paper

Minimizing Message Size in Stochastic Communication Patterns: Fast Self-Stabilizing Protocols with 3 bits

Lucas Boczkowski
Amos Korman
Emanuele Natale

This paper considers the basic PULL model of communication, in which in each round, each agent extracts information from few randomly chosen agents. We seek to identify the smallest amount of information revealed in each interaction (message size) that nevertheless allows for efficient and robust computations of fundamental information dissemination tasks. We focus on the Majority Bit Dissemination problem that considers a population of n agents, with a designated subset of source agents. Each source agent holds an input bit and each agent holds an output bit. The goal is to let all agents converge their output bits on the most frequent input bit of the sources (the majority bit). Note that the particular case of a single source agent corresponds to the classical problem of Broadcast (also termed Rumor Spreading). We concentrate on the severe fault-tolerant context of self-stabilization, in which a correct configuration must be reached eventually, despite all agents starting the execution with arbitrary initial states. In particular, the specification of who is a source and what is its initial input bit may be set by an adversary. We first design a general compiler which can essentially transform any self-stabilizing algorithm with a certain property (called “the bitwise-independence property” ) that uses ℓ-bits messages to one that uses only log ¿-bits messages, while paying only a small penalty in the running time. By applying this compiler recursively we then obtain a self-stabilizing Clock Synchronization protocol, in which agents synchronize their clocks modulo some given integer T, within Õ(log n log T ) rounds w. h. p. , and using messages that contain 3 bits only. We then employ the new Clock Synchronization tool to obtain a self-stabilizing Majority Bit Dissemination protocol which converges in Õ(log n ) time, w. h. p. , on every initial configuration, provided that the ratio of sources supporting the minority opinion is bounded away from half. Moreover, this protocol also uses only 3 bits per interaction.

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MFCS Conference 2016 Conference Paper

On the Voting Time of the Deterministic Majority Process

Dominik Kaaser
Frederik Mallmann-Trenn
Emanuele Natale

In the deterministic binary majority process we are given a simple graph where each node has one out of two initial opinions. In every round, each node adopts the majority opinion among its neighbors. It is known that this process always converges in O(|E|) rounds to a two-periodic state in which every node either keeps its opinion or changes it in every round. It has been shown by Frischknecht, Keller, and Wattenhofer (2013) that the O(|E|) bound on the convergence time of the deterministic binary majority process is even for dense graphs tight. However, in many graphs such as the complete graph the process converges in just a constant number of rounds from any initial opinion assignment. We show that it is NP-hard to decide whether there exists an initial opinion assignment for which it takes more than k rounds to converge to the two-periodic stable state, for a given integer k. We then give a new upper bound on the voting time of the deterministic binary majority process. Our bound can be computed in linear time by carefully exploiting the structure of the potential function by Goles and Olivos. We identify certain modules of a graph G to obtain a new graph G^Delta. This new graph G^Delta has the property that the worst-case convergence time of G^Delta is an upper bound on that of G. Our new bounds asymptotically improve the best known bounds for various graph classes.

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SODA Conference 2016 Conference Paper

Stabilizing Consensus with Many Opinions

Luca Becchetti
Andrea Clementi
Emanuele Natale
Francesco Pasquale
Luca Trevisan 0001

We consider the following distributed consensus problem: Each node in a complete communication network of size n initially holds an opinion, which is chosen arbitrarily from a finite set Σ. The system must converge toward a consensus state in which all, or almost all nodes, hold the same opinion. Moreover, this opinion should be valid, i. e. , it should be one among those initially present in the system. This condition should be met even in the presence of a malicious adversary who can modify the opinions of a bounded subset of nodes, adaptively chosen in every round. We consider the 3-majority dynamics: At every round, every node pulls the opinion from three random neighbors and sets his new opinion to the majority one (ties are broken arbitrarily). Let k be the number of valid opinions. We show that, if k ≤ n α, where α is a suitable positive constant, the 3-majority dynamics converges in time polynomial in k and log n with high probability even in the presence of an adversary who can affect up to nodes at each round. Previously, the convergence of the 3-majority protocol was known for |Σ| = 2 only, with an argument that is robust to adversarial errors. On the other hand, no anonymous, uniform-gossip protocol that is robust to adversarial errors was known for |Σ| > 2.

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TCS Journal 2015 Journal Article

Distributed community detection in dynamic graphs

Andrea Clementi
Miriam Di Ianni
Giorgio Gambosi
Emanuele Natale
Riccardo Silvestri

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied Planted Bisection Model dyn- G ( n, p, q ) where the node set [ n ] of the network is partitioned into two unknown communities and, at every time step, each possible edge ( u, v ) is active with probability p if both nodes belong to the same community, while it is active with probability q (with q ≪ p ) otherwise. We also consider a time-Markovian generalization of this model. We propose a distributed protocol based on the popular Label-Propagation approach and prove that, when the ratio p / q is larger than n b (for an arbitrarily small constant b > 0 ), the protocol finds the right “planted” partition in O ( log ⁡ n ) time even when the snapshots of the dynamic graph are sparse and disconnected (i. e. , when p = Θ ( 1 / n ) ).

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SODA Conference 2015 Conference Paper

Plurality Consensus in the Gossip Model

Luca Becchetti
Andrea Clementi
Emanuele Natale
Francesco Pasquale
Riccardo Silvestri

We study Plurality Consensus in the Model over a network of n anonymous agents. Each agent supports an initial opinion or color. We assume that at the onset, the number of agents supporting the plurality color exceeds that of the agents supporting any other color by a sufficiently-large bias, though the initial plurality itself might be very far from absolute majority. The goal is to provide a protocol that, with high probability, brings the system into the configuration in which all agents support the (initial) plurality color. We consider the Undecided-State Dynamics, a well-known protocol which uses just one more state (the undecided one) than those necessary to store colors. We show that the speed of convergence of this protocol depends on the initial color configuration as a whole, not just on the gap between the plurality and the second largest color community. This dependence is best captured by a novel notion we introduce, namely, the monochromatic distance md( ) which measures the distance of the initial color configuration from the closest monochromatic one. In the complete graph, we prove that, for a wide range of the input parameters, this dynamics converges within O (md( ) log n ) rounds. We prove that this upper bound is almost tight in the strong sense: Starting from any color configuration, the convergence time is Ω(md( )). Finally, we adapt the Undecided-State Dynamics to obtain a fast, random walk-based protocol for plurality consensus on regular expanders. This protocol converges in O (md( ) polylog( n )) rounds using only polylog( n ) local memory. A key-ingredient to achieve the above bounds is a new analysis of the maximum node congestion that results from performing n parallel random walks on regular expanders. All our bounds hold with high probability.

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