First convergence analysis of DPO under federated and decentralized training, characterizing rates via client drift, communication frequency, preference heterogeneity, and graph spectral connectivity.
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On the convergence of FedA vg on non-IID data
19 Pith papers cite this work. Polarity classification is still indexing.
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Semantic consensus on model outputs for public prompts enables federated LLM fine-tuning that matches parameter-aggregation baselines with orders-of-magnitude lower communication.
Derives tractable optimal fair multi-class classifier and supplies in-processing and post-processing algorithms that converge to the accuracy-fairness Pareto frontier.
A device-partitioning bandwidth allocation policy for federated learning over IIoT networks that provably reduces total training time compared to any non-partitioning scheme.
Nonlinear kernel integration (NKI) with graph regularization enables accurate alignment of nonlinearly obfuscated decentralized data representations, outperforming linear methods on image classification.
Introduces FedHybrid and FedNewton for DP federated M-estimation, with finite-sample MSE bounds, minimax lower bound, and evaluations on vision datasets.
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
FedVSSAM mitigates flatness incompatibility in SAM-based federated learning by consistently using a variance-suppressed adjusted direction for local perturbation, descent, and global updates, with non-convex convergence guarantees.
ChainFed achieves memory-efficient private LLM fine-tuning on edge devices through sequential layer-by-layer adapter training with dynamic co-tuning, perceptive optimization, and adaptive starting point selection, improving accuracy by up to 46.46%.
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
FIRMA introduces Fibonacci ring aggregation protocols for server-free federated learning that maintain private heads and achieve higher accuracy than FedAvg under label skew across multiple benchmarks and heterogeneity regimes.
FedGMI applies VAEs as density estimators in federated learning to infer mixture proportions of shared distributions for structured personalization under data heterogeneity.
FedPLT assigns client-specific model layers for training and matches or beats full-model federated learning accuracy with 71-82 percent fewer trainable parameters per client.
AdaBFL introduces a novel three-layer adaptive aggregation mechanism for Byzantine-robust federated learning that counters complex attacks, provides non-convex non-iid convergence guarantees, and shows superior performance in experiments.
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.
Relay-assisted partial aggregation with device grouping, relay selection, and SPCA-based power optimization reduces energy consumption by 2-6x and outage probability to 10^-6 in IIoT federated learning.
Non-identical data distributions degrade federated averaging accuracy on visual classification, but server momentum raises CIFAR-10 accuracy from 30.1% to 76.9% in the most skewed regimes.
A closed-form FL convergence upper bound incorporating sensing SNR, dataset size, and transmission reliability enables joint optimization of sensing power, snapshots, and communication power in ISAC systems.
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
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Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
Introduces FedHybrid and FedNewton for DP federated M-estimation, with finite-sample MSE bounds, minimax lower bound, and evaluations on vision datasets.