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Federated Learning (FL) enables model training across decentralized clients while preserving data privacy. However, bandwidth constraints limit the volume of information exchanged, making communication efficiency a critical challenge. In addition, non- IID data distributions require fairness-aware mechanisms to prevent performance degradation for certain clients. Existing sparsification techniques often apply fixed compression ratios uniformly, ignoring variations in client importance and bandwidth. We propose FedBand, a dynamic bandwidth allocation framework that prioritizes clients based on their contribution to the global model. Unlike conventional approaches, FedBand does not enforce uniform client participation in every communication round. Instead, it allocates more bandwidth to clients whose local updates deviate significantly from the global model, enabling them to transmit a greater number of parameters. Clients with less impactful updates contribute proportionally less or may defer transmission, reducing unnecessary overhead while maintaining generalizability. By optimizing the trade-off between communication efficiency and learning performance, FedBand substantially reduces transmission costs while preserving model accuracy. Experiments on non-IID CIFAR-10 and UTMobileNet2021 datasets, demonstrate that FedBand achieves up to 99.81% bandwidth savings per round while maintaining accuracies close to that of an unsparsified model (80% on CIFAR- 10, 95% on UTMobileNet), despite transmitting less than 1% of the model parameters in each round. Moreover, FedBand accelerates convergence by 37.4%, further improving learning efficiency under bandwidth constraints. Mininet emulations further show a 42.6% reduction in communication costs and a 65.57% acceleration in convergence compared to baseline methods, validating its real-world efficiency. These results demonstrate that adaptive bandwidth allocation can significantly enhance the scalability and communication efficiency of federated learning, making it more viable for real- world, bandwidth-constrained networking environments. 

This report presents a comprehensive collection of searches for new physics performed by the ATLAS Collaboration during the Run~2 period of data taking at the Large Hadron Collider, from 2015 to 2018, corresponding to about 140~$$^{-1}$$ of $$\sqrt{s}=13$$~TeV proton--proton collision data. These searches cover a variety of beyond-the-standard model topics such as dark matter candidates, new vector bosons, hidden-sector particles, leptoquarks, or vector-like quarks, among others. Searches for supersymmetric particles or extended Higgs sectors are explicitly excluded as these are the subject of separate reports by the Collaboration. For each topic, the most relevant searches are described, focusing on their importance and sensitivity and, when appropriate, highlighting the experimental techniques employed. In addition to the description of each analysis, complementary searches are compared, and the overall sensitivity of the ATLAS experiment to each type of new physics is discussed. Summary plots and statistical combinations of multiple searches are included whenever possible. 

The ATLAS experiment has developed extensive software and distributed computing systems for Run 3 of the LHC. These systems are described in detail, including software infrastructure and workflows, distributed data and workload management, database infrastructure, and validation. The use of these systems to prepare the data for physics analysis and assess its quality are described, along with the software tools used for data analysis itself. An outlook for the development of these projects towards Run 4 is also provided. 

A search is performed for dark matter particles produced in association with a resonantly produced pair of b-quarks with 30 < mbb < 150 GeV using 140 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the LHC. This signature is expected in extensions of the standard model predicting the production of dark matter particles, in particular those containing a dark Higgs boson s that decays into bb¯. The highly boosted s → bb¯ topology is reconstructed using jet reclustering and a new identification algorithm. This search places stringent constraints across regions of the dark Higgs model parameter space that satisfy the observed relic density, excluding dark Higgs bosons with masses between 30 and 150 GeV in benchmark scenarios with Z0 mediator masses up to 4.8 TeV at 95% confidence level. 

A<sc>bstract</sc> The paper presents a search for supersymmetric particles produced in proton-proton collisions at$$ \sqrt{s} $$ $\sqrt{s}$= 13 TeV and decaying into final states with missing transverse momentum and jets originating from charm quarks. The data were taken with the ATLAS detector at the Large Hadron Collider at CERN from 2015 to 2018 and correspond to an integrated luminosity of 139 fb⁻¹. No significant excess of events over the expected Standard Model background expectation is observed in optimized signal regions, and limits are set on the production cross-sections of the supersymmetric particles. Pair production of charm squarks or top squarks, each decaying into a charm quark and the lightest supersymmetric particle$$ {\overset{\sim }{\chi}}_1^0 $$ ${\tilde{\chi }}_1^0$, is excluded at 95% confidence level for squarks with masses up to 900 GeV for scenarios where the mass of$$ {\overset{\sim }{\chi}}_1^0 $$ ${\tilde{\chi }}_1^0$is below 50 GeV. Additionally, the production of leptoquarks with masses up to 900 GeV is excluded for the scenario where up-type leptoquarks decay into a charm quark and a neutrino. Model-independent limits on cross-sections and event yields for processes beyond the Standard Model are also reported. 

Abstract The ATLAS tile calorimeter (TileCal) is the hadronic sampling calorimeter covering the central region of the ATLAS detector at the Large Hadron Collider (LHC). This paper gives an overview of the calorimeter’s operation and performance during the years 2015–2018 (Run 2). In this period, ATLAS collected proton–proton collision data at a centre-of-mass energy of 13 TeV and the TileCal was 99.65% efficient for data-taking. The signal reconstruction, the calibration procedures, and the detector operational status are presented. The performance of two ATLAS trigger systems making use of TileCal information, the minimum-bias trigger scintillators and the tile muon trigger, is discussed. Studies of radiation effects allow the degradation of the output signals at the end of the LHC and HL-LHC operations to be estimated. Finally, the TileCal response to isolated muons, hadrons and jets from proton–proton collisions is presented. The energy and time calibration methods performed excellently, resulting in good stability and uniformity of the calorimeter response during Run 2. The setting of the energy scale was performed with an uncertainty of 2%. The results demonstrate that the performance is in accordance with specifications defined in the Technical Design Report. 

Z boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from standard model predictions. All previous measurements of Z boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins. In this analysis, a machine learning method called omnifold is used to produce a simultaneous measurement of twenty-four Z+jets observables using 139 /fb of proton-proton collisions at sqrt(s) =  TeV collected with the ATLAS detector. Unlike any previous fiducial differential cross-section measurement, this result is presented unbinned as a dataset of particle-level events, allowing for flexible reuse in a variety of contexts and for new observables to be constructed from the twenty-four measured observables. 

A<sc>bstract</sc> A search is presented for new particles produced in proton-proton collisions at a centre-of-mass energy of 13 TeV that result in final states comprising a massive vector (WorZ) boson that decays hadronically and large missing transverse momentum. The data sample was collected with the ATLAS experiment at the Large Hadron Collider from 2015 to 2018 and corresponds to an integrated luminosity of 140 fb⁻¹. No significant excess over the Standard Model expectation is observed. Model-independent 95% confidence-level limits on the visible cross-section that range from 0.3 fb to 79.5 fb are obtained for non-Standard-Model processes. Exclusion limits are also presented for models with axion-like particles, for two-Higgs-doublet models with a pseudo-scalar mediator between the Standard Model and the dark sector, for the invisible decay of the Higgs boson and for pair-produced weakly interacting dark matter candidates.