LOSCAR-SGD combines local updates, sparse model averaging, and communication-computation overlap with a delay-corrected merge rule, providing convergence rates for smooth non-convex objectives under worker heterogeneity.
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Canonical reference. 80% of citing Pith papers cite this work as background.
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representative citing papers
PMF-CL derives Pareto-minimal-forgetting algorithms for linear/basis-function regression and quadratic-bounded losses like logistic regression, achieving static O(d²) memory for d-parameter models.
Introduces the CULT threat model with four circuit-level attacks on quantum federated learning and shows they degrade accuracy on MNIST and CIFAR-10 even when defenses like Krum are used.
Ringmaster LMO extends delay-thresholding from ASGD to LMO-based momentum updates, providing convergence guarantees under (L0, L1)-smoothness and time-complexity bounds that recover optimal rates in the Euclidean case.
Real 5G testbed experiments show consistent stragglers in 70% of federated learning trials due to communication delays, challenging common wireless FL assumptions.
Prefix-tuning matches or exceeds fine-tuning on NLG tasks by optimizing a continuous prefix using 0.1% of parameters while keeping the LM frozen.
Algorithm for group distributionally robust linear regression using block Lewis weights to achieve (1+ε) optimality in Õ(min{rank(A), m}^{1/3} ε^{-2/3}) linear-system solves.
FLKit is a new toolkit with four lifecycle stages, eleven role-specific entry points, a glossary, FL Story template, and tool directory to support federated learning projects in health and life sciences.
HADES selectively encrypts privacy-sensitive features identified by PCA in federated learning, trains hybrid encrypted and plaintext networks, and fuses them to match vanilla FL accuracy with reduced overhead and better privacy.
User memory in LLMs factors into three orthogonal axes where parametric adapters and retrieval show opposite strengths, with causal evidence from attention interventions and an alignment tax on RLHF models.
A multimodal graph learning method for V2X beam alignment cuts overhead by over 90% and outperforms prior federated learning baselines under label and modality imbalance.
Decoupled DiLoCo enables asynchronous distributed pre-training with zero global downtime under simulated failures while preserving competitive performance on text and vision tasks.
Random Network Distillation enables pre-training discovery of client clusters in federated learning via local novelty signals, supporting autonomous grouping under non-IID data without a priori cluster count.
EH-FedSAG achieves higher test accuracy and lower training variance than EH-FedAvg in simulations of energy-harvesting federated learning for both homogeneous and heterogeneous data, with larger gains under scarce energy.
C2FL proposes spatial clustering plus continual learning techniques inside federated learning to maintain performance under combined spatial heterogeneity and temporal drift.
FPLIER performs federated PLIER training via secure aggregation that is algebraically equivalent to centralized training, with membership-inference risk shown to decrease as the rank of the expression matrix increases.
A federated intrusion detection method combines hybrid Naive Bayes classifiers as a mixture of Gaussians and uses a governance-derived Institutional Coherence Index to regularize server-side weights via Nelder-Mead optimization, reporting F1 gains over size-proportional averaging on three datasets.
BiFedKD improves ECG classification accuracy by 3.52% and Macro-F1 by 9.93% on MIT-BIH while cutting communication overhead 40% and computation cost 71.7% versus baseline federated methods.
Rennala MVR improves time complexity over Rennala SGD for smooth nonconvex stochastic optimization in heterogeneous parallel systems under a mean-squared smoothness assumption.
Experiments on real industrial time series show that partial model sharing improves diffusion model performance in bandwidth-limited non-IID settings, while full sharing stabilizes GAN training but offers less robustness than VAE or DDPM alternatives.
Federated learning on 310 CT scans from two centers yields pediatric OAR segmentation models with better cross-center robustness than local models for nine evaluated structures.
SplitFT adapts cut-layer selection and reduces LoRA rank per client in federated split learning to improve efficiency and performance when fine-tuning LLMs on heterogeneous devices and data.
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.
FoggyTrust is a hierarchical extension of FLTrust that localizes trust computation to fog nodes and combines it with heterogeneity-aware optimizers, reporting over 50% gains on CIFAR-10 under Krum and Trim attacks.
citing papers explorer
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LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
LOSCAR-SGD combines local updates, sparse model averaging, and communication-computation overlap with a delay-corrected merge rule, providing convergence rates for smooth non-convex objectives under worker heterogeneity.
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PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks
PMF-CL derives Pareto-minimal-forgetting algorithms for linear/basis-function regression and quadratic-bounded losses like logistic regression, achieving static O(d²) memory for d-parameter models.
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Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
Introduces the CULT threat model with four circuit-level attacks on quantum federated learning and shows they degrade accuracy on MNIST and CIFAR-10 even when defenses like Krum are used.
-
Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method
Ringmaster LMO extends delay-thresholding from ASGD to LMO-based momentum updates, providing convergence guarantees under (L0, L1)-smoothness and time-complexity bounds that recover optimal rates in the Euclidean case.
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Beyond Assumptions: Measuring Federated Learning over Real 5G Networks
Real 5G testbed experiments show consistent stragglers in 70% of federated learning trials due to communication delays, challenging common wireless FL assumptions.
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Prefix-Tuning: Optimizing Continuous Prompts for Generation
Prefix-tuning matches or exceeds fine-tuning on NLG tasks by optimizing a continuous prefix using 0.1% of parameters while keeping the LM frozen.
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Distributionally Robust Linear Regression With Block Lewis Weights
Algorithm for group distributionally robust linear regression using block Lewis weights to achieve (1+ε) optimality in Õ(min{rank(A), m}^{1/3} ε^{-2/3}) linear-system solves.
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Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences
FLKit is a new toolkit with four lifecycle stages, eleven role-specific entry points, a glossary, FL Story template, and tool directory to support federated learning projects in health and life sciences.
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HADES: Privacy-Preserving Federated Learning via Selective Feature Encryption and Hybrid Model Fusion
HADES selectively encrypts privacy-sensitive features identified by PCA in federated learning, trains hybrid encrypted and plaintext networks, and fuses them to match vanilla FL accuracy with reduced overhead and better privacy.
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Substrate Asymmetry in User-Side Memory: A Diagnostic Framework
User memory in LLMs factors into three orthogonal axes where parametric adapters and retrieval show opposite strengths, with causal evidence from attention interventions and an alignment tax on RLHF models.
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Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach
A multimodal graph learning method for V2X beam alignment cuts overhead by over 90% and outperforms prior federated learning baselines under label and modality imbalance.
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Decoupled DiLoCo for Resilient Distributed Pre-training
Decoupled DiLoCo enables asynchronous distributed pre-training with zero global downtime under simulated failures while preserving competitive performance on text and vision tasks.
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Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning
Random Network Distillation enables pre-training discovery of client clusters in federated learning via local novelty signals, supporting autonomous grouping under non-IID data without a priori cluster count.
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EH-FedSAG: Variance-Reduced Federated Learning with Energy-Aware Participation in Energy-Harvesting IoT
EH-FedSAG achieves higher test accuracy and lower training variance than EH-FedAvg in simulations of energy-harvesting federated learning for both homogeneous and heterogeneous data, with larger gains under scarce energy.
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C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift
C2FL proposes spatial clustering plus continual learning techniques inside federated learning to maintain performance under combined spatial heterogeneity and temporal drift.
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FPLIER: Federated Pathway-Level Information Extractor
FPLIER performs federated PLIER training via secure aggregation that is algebraically equivalent to centralized training, with membership-inference risk shown to decrease as the rank of the expression matrix increases.
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Federated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection
A federated intrusion detection method combines hybrid Naive Bayes classifiers as a mixture of Gaussians and uses a governance-derived Institutional Coherence Index to regularize server-side weights via Nelder-Mead optimization, reporting F1 gains over size-proportional averaging on three datasets.
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BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring
BiFedKD improves ECG classification accuracy by 3.52% and Macro-F1 by 9.93% on MIT-BIH while cutting communication overhead 40% and computation cost 71.7% versus baseline federated methods.
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Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
Rennala MVR improves time complexity over Rennala SGD for smooth nonconvex stochastic optimization in heterogeneous parallel systems under a mean-squared smoothness assumption.
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On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
Experiments on real industrial time series show that partial model sharing improves diffusion model performance in bandwidth-limited non-IID settings, while full sharing stabilizes GAN training but offers less robustness than VAE or DDPM alternatives.
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Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy
Federated learning on 310 CT scans from two centers yields pediatric OAR segmentation models with better cross-center robustness than local models for nine evaluated structures.
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SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
SplitFT adapts cut-layer selection and reduces LoRA rank per client in federated split learning to improve efficiency and performance when fine-tuning LLMs on heterogeneous devices and data.
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Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution
CPPDD is a new consensus-based protocol for privacy-preserving multi-client data sharing that achieves unanimous-release confidentiality, linear scalability, and high-probability malicious deviation detection.
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FoggyTrust: Robust Federated Learning with Hierarchical Trust Networks
FoggyTrust is a hierarchical extension of FLTrust that localizes trust computation to fog nodes and combines it with heterogeneity-aware optimizers, reporting over 50% gains on CIFAR-10 under Krum and Trim attacks.
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FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals
FLFL extends latent factor learning into a federated framework that recovers missing spatio-temporal signals in wireless sensor networks by sharing gradients and enforcing spatio-temporal regularization.
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ScaleAcross: Designing Multi-Data-Center Infrastructure for Geo-Distributed AI Training
Presents an EVPN-VXLAN emulation framework with ECMP, BFD, and queue-pair traffic distribution for studying AllReduce and Parameter Server patterns in geo-distributed AI training.
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SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks
SwarmHarness is a proposed decentralized protocol for compute sharing among AI agents via DHT registry, load-aware routing, and credit incentives that penalize non-contributors.
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Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable
Life-logging video streams create an inevitable privacy-utility trade-off that is a foundational challenge for always-on AI systems.
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The Impact of Federated Learning on Distributed Remote Sensing Archives
FedProx outperforms FedAvg for deeper models under data heterogeneity, BSP reaches near-centralized accuracy at high communication cost, and LeNet gives the best accuracy-communication trade-off on the UC Merced dataset.
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Pseudoconvex Problems in Operational Decision Systems: Algorithms for Joint Learning and Optimization
Iterative joint learning-optimization framework with convergent algorithms for pseudoconvex objectives in operational decision systems.
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Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data
Introduces M-DSL algorithm for distributed swarm learning that selects workers using a new non-i.i.d. degree metric to improve convergence and accuracy under data heterogeneity, with theoretical analysis and experiments on heterogeneous datasets.
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Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey
The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.
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The Role of Artificial Intelligence in the SKA Era
This review chapter maps SKA data volume, complexity, and interpretability challenges onto deep learning, generative models, reinforcement learning, and federated learning for source detection, calibration, and discovery.
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Machine Unlearning: A Comprehensive Survey
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.
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Data Aggregation Techniques for Internet of Things
Proposes three approaches for IoT data aggregation: D2D-based clustering for energy efficiency in stationary/mobile nodes, a scheme to improve quality of uncertain raw data, and a prediction-based framework for massive medical IoT devices.
- Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation