Good Papers

Showing Federated learning Show all papers

80%Must read
?Must readVote to see the score
arXivFederated learning

FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices

FedSAP uses budget-constrained tri-state channel allocation to partition models into global, private, and dropped channels for heterogeneous federated domain generalization, improving accuracy by up to 4.92 points under 80% pruning.

Wentao Yue, Tianyou Lai, Hongji Li, Qingyu Mao and 1 more

Published Oct 1, 2026 · 0 citations

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Guangdong University of TechnoloFederated learning

Reliable Federated Multi-View Learning via Conflict-Aware Evidence Calibration

Daoyuan Li, Zuyuan Yang, Hao Yang, Jiawen Kang

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026NankaiTianjinFederated learning

Confounding-Aware Client Selection in Federated Learning via Causal Mediation Analysis

Xiaoyang Yi, Yuru Bao, Rihao Chang, Binhan Yang and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Southwestern University of FinanThe Hong Kong University of ScieHong Kong PolytechnicWeBank and HKUSTFederated learning

FedVaccine: Knowledge Recall after Spatial-Temporal Catastrophic Forgetting in Federated Continual Learning via Gradient-Based Vaccine

Hao Yu, Xin Yang, Boyang Fan, Xuemei Cao and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U Electronic Science and TechnolSingapore ManagementUniversiti MalayaFederated learning

Geometry-Aware Subspace Perturbation for Heterogeneous Federated Learning

Xiangtao Zhang, Hailong Yan, Obed Irihose, Joey Tianyi Zhou and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026ShandongNational University of SingaporeNanyang Technological UniversityNanyang TechnologicalFederated learning

Geometry-Regularized Collapse Resistance via Consensus Enhancement for Federated Learning

Jingjing Zhu, Zhuang Qi, Lei Meng, Han Yu and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Nanyang TechnologicalNational University of SingaporeNanjing University of Posts and Nanyang Technological UniversitySouth China University of TechnoFederated learning

Distributionally Robust Black Box Optimization-based Bidding Strategy in Auction-based Federated Learning

Xiaoli Tang, Zhuang Qi, Ying-Peng Tang, Xianjie Guo and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Beijing University of TechnologyZhejiang NormalFederated learning

Fed-AGA: An Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering

Yichi Zhang, bohang sun, Hao Wei, Kai Di and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026CornellFederated learning

The Price of Locality: Why Forward-Forward Underperforms Backpropagation?

Zhaoxian Wu, Haichuan Liu, Tianyi Chen

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Beijing University of AeronauticBeihangNanyang TechnologicalFederated learning

Stability-Enhanced Federated Learning with Accelerated Gradient

Tianxiang Chen, Wenjie Hou, Feng Wang, Tiantong Wang and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U AthensAthena Research and Innovation CAthens University of Economics aPolyShapeFederated learning

A typed tensor language for federated learning

Theofilos Mailis, Kalliopi-Christina Despotidou, Konstantinos Filippopolitis, Yannis Foufoulas and 5 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Institute for Data Science in MeRheinisch Westfälische TechnischRWTH AachenFederated learning

Efficient Knowledge Transfer in Federated Bayesian Optimization through Neural Network Surrogates

Alexander Gräfe, Max van Gemmeren, Paul Brunzema, Sebastian Trimpe

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026SoutheastTongjiShanghai Jiao TongHong Kong PolytechnicMohamed bin Zayed University of Federated learning

FedSEM: Mitigating Cross-Client Evidence Drift in Federated Multiple Instance Learning

Zhiqiang Kou, Beidi Wang, Yuling Shi, Shengkun Zhu and 5 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Stevens Institute of TechnologyQueen's University BelfastUIUCDepartment of Computer Science, Federated learning

Bayes-pFCL:Bayesian Personalized Federated Continual Learning

qingyang yu, Yang Hua, Hao Wang, Yue Ning and 2 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026ETH ZurichETHZ - ETH ZurichFederated learning

Federated Logic Gate Networks via Boolean Feature Selection

Felix Kunze, Antonio Di Maio

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Shanghai University of Electric Shanghai university of electriciEast China NormalU Technology SydneyFederated learning

FedPeel: Peeling Stabilized Layers for Robust Heterogeneous Federated Learning

Haizhou Du, Lixin Huang, Zonghan Wu, Huan Huo and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Nanyang TechnologicalFederated learning

Pointillism: Probing-Based Model Compatibility for Robust Collaborative Machine Learning

Yang Li, Adams Wai Kin Kong

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026EPFLGSAFederated learning

Robust and Scalable Collaborative Learning via Pull-Based Epidemic Communication

Abdellah El Mrini, Sadegh Farhadkhani, Rachid Guerraoui

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026KoreaFederated learning

Constant Term Shrinkage for Federated Learning

Incheol Baek, Minseo Kim, Hyeonmin Kang, Yon Dohn Chung

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026TempleState University of New York, StFederated learning

A Doubly Smoothed Decentralized Stochastic Minimax Optimization Algorithm

Xinwen Zhang, Chiu C Tan, Haibin Ling, Hongchang Gao

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Beijing University of Posts and Huazhong University of Science aMohamed bin Zayed University of Shanghai Artificial IntelligenceMBZUAIFederated learning

Heterogeneity-aware Distillation for Federated Continual Learning

Gaozhuo Liu, Yichen Li, Xiuying Wang, Yulong Li and 3 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Beijing University of Posts and Huazhong University of Science aQueen Mary, University of LondonMohamed bin Zayed University of Shenzhen University of Advanced Federated learning

Rehearsal-Free Statistical Prototype Regularization for Federated Incremental Learning

Xiuying Wang, Yichen Li, Jiahua Cheng, Xiwei Liu and 3 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026U Maryland, College ParkU Texas at ArlingtonU Maryland College ParkFederated learning

Mitigating Data Heterogeneity Effect in Client-Reshuffling-Based Federated Learning

Su Zhang, Peiran Yu, Heng Huang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Harbin Institute of Technology SFederated learning

Nerve-Skeleton Message Passing for Federated Optimization with Overlapping Parameters

Wei Zhang

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026PurdueFederated learning

Beyond Flat Gossip: Tiered Gossip Learning for Scalable Collaborative AI

Atul Sharma, Kavindu Herath, Saurabh Bagchi, Chaoyue Liu and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
91%Must read
?Must readVote to see the score
NeurIPS 2026VinUniversityCAIR, VinUniversityFederated learning

HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning

HERO introduces a heterogeneity-aware benchmark library for federated continual learning that separates task splits, client splits, and sequences to expose hidden performance disparities.

Thinh Nguyen, Le-Tuan Nguyen, Minh-Duong Nguyen, Nhi Trinh and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
17/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 17 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 3/5
78%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026U LeicesterNanchangZhejiangU ExeterFederated learning

FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation

FedAdaVR combines adaptive optimization with variance reduction using stored updates to eliminate partial participation error in federated learning, and FedAdaVR-Quant cuts memory by up to 87.5%.

S M RUHUL KABIR HOWLADER, Xiao Chen, Yifei Xie, Lu Liu

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 1/5
80%Must read
?Must readVote to see the score
NeurIPS 2026FudanETH Zurich / HKUEdith CowanSUTDFederated learning

FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning

FedVSSAM fixes flatness incompatibility in federated sharpness-aware learning via variance-suppressed global directions to boost convergence and generalization.

Bingnan Xiao, Yuan Gao, Bingcong Li, Wei Ni and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 3/5
medium 7/10
strict 2/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Criteo AI LabCentraleSupélecFederated learning

CUVET: A Partitioning Approach for Continuous Treatment Assignment At Scale

CUVET partitions continuous treatment assignment at scale via a scalable partitioning approach that improves estimation and assignment efficiency.

Artem Betlei, Mariia Vladimirova, Victor Girou, Thibaud Rahier

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
78%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Carnegie MellonNVIDIATikTokFederated learning

Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning

FedRevive revives stale asynchronous federated updates via server-side data-free knowledge distillation, accelerating training by 38.4% and boosting accuracy by 16.5%.

Baris Askin, Holger Roth, Zhenyu Sun, Carlee Joe-Wong and 2 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
86%Must read
?Must readVote to see the score
NeurIPS 2026PurdueU Colorado Colorado SpringsFederated learning

Communication-Efficient Personalized Adaptation via Federated-Local Model Merging

Potara merges federated and local models via closed-form optimal weights to improve federated personalization with lower communication costs.

Yinan Zou, Md Kamran Chowdhury Shisher, Christopher Brinton, Vishrant Tripathi

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 1/5
80%Must read
?Must readVote to see the score
NeurIPS 2026Technion - Israel Institute of TTechnionFederated learning

SP-CACW: Convergence-Aware Client Weighting for Selfish Personalized Learning

SP-CACW minimizes an upper bound on a target client's convergence error via convergence-aware weighting that trades peer bias against variance and excludes harmful peers.

Yaron Kiselman, Kfir Y. Levy

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 2/5
80%Must read
?Must readVote to see the score
NeurIPS 2026INRIAEcole PolytechniqueInriaFederated learning

Principled Federated Random Forests for Heterogeneous Data

FedForest proposes a federated random forest using aggregated statistics to approximate centralized splits under heterogeneous data, enabling personalized client-indicator splits with near-centralized accuracy and low communication cost.

Rémi Khellaf, Erwan Scornet, Aurélien Bellet, Julie Josse

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5
86%Must read
?Must readVote to see the score
NeurIPS 2026EPFLKAUSTFederated learning

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity

Rescaled ASGD corrects asynchronous SGD's bias toward fast workers via computation-time-proportional step sizes, matching optimal time complexity with only lower-order heterogeneity penalties.

Ammar Mahran, Artavazd Maranjyan, Peter Richtarik

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 2/5
72%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Indian Institute of ScienceDelft University of TechnologyIndian Institute of Technology, SyracuseFederated learning

Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation

FedUCA treats servers as participation optimizers to sustain rational clients, achieving higher retention and better global models than standard aggregation.

Yashwanth Mandula, Arunabh Singh, Ashok Nayak, Saikiran Bulusu and 1 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
8/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 8 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 0/5