Leaf: A benchmark for federated settings

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• From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning cs.AI · 2026-05-07 · unverdicted · none · ref 49

  FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.

• Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy cs.LG · 2026-04-24 · unverdicted · none · ref 2

  Matrix von Neumann entropy of final-layer gradients acts as a data-free proxy for client contribution in federated learning, showing high correlation with standalone accuracy on non-IID benchmarks.

• SMART: A Spectral Transfer Approach to Multi-Task Learning cs.LG · 2026-04-22 · unverdicted · none · ref 81

  SMART transfers knowledge in multi-task linear regression via spectral subspace similarity assumptions, achieving near-minimax Frobenius error rates while requiring only a fitted source model.

• SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening cs.LG · 2025-10-09 · accept · none · ref 10

  SketchGuard decouples Byzantine filtering from aggregation in decentralized federated learning by exchanging k-dimensional Count Sketches for screening and full models only from accepted neighbors, achieving up to 50-70% communication savings while proving convergence and matching SOTA robustness.

• FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation cs.LG · 2025-06-26 · conditional · none · ref 6

  FeDa4Fair is a new library and benchmark for creating federated datasets with heterogeneous client-level biases to standardize evaluation of fairness methods in federated learning.

• Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits cs.LG · 2026-05-14 · unverdicted · none · ref 5

  Adapting multi-objective pure-exploration bandits enables efficient Pareto prompt set recovery and best feasible prompt identification for LLMs, with linear-case guarantees and empirical gains over baselines.

• Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity cs.LG · 2026-05-13 · unverdicted · none · ref 4

  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.

• PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning cs.LG · 2026-04-14 · unverdicted · none · ref 18

  PrivEraserVerify unifies efficiency via adaptive checkpointing, privacy via layer-adaptive DP, and verifiability via fingerprints in federated unlearning, claiming 2-3x faster performance than retraining with formal guarantees.

• Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration cs.DC · 2025-12-05 · unverdicted · none · ref 1

  FedGMR progressively restores sub-model capacity for bandwidth-constrained clients via gradual density increases and mask-aware aggregation, narrowing the gap to full-model federated learning.

• Incentivizing Honesty among Competitors in Collaborative Learning and Optimization cs.LG · 2023-05-25 · unverdicted · none · ref 7

  Formulates a game where competitors in collaborative learning are incentivized to manipulate updates, then proposes mechanisms that restore honest participation for mean estimation, convex SGD, and non-convex federated learning.

• Adaptive Federated Optimization cs.LG · 2020-02-29 · unverdicted · none · ref 209

  Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.

• CRAFT: Conflict-Resolved Aggregation for Federated Training cs.LG · 2026-05-20 · unverdicted · none · ref 1

  CRAFT derives a closed-form solution for conflict-resolved aggregation in federated learning via geometric constraints and projection, with theoretical support for common descent and empirical gains on heterogeneous data.

• FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing cs.LG · 2026-05-07 · unverdicted · none · ref 3

  FedFrozen improves stability in heterogeneous federated Transformer training by warming up the full model then freezing the attention kernel (query/key) while optimizing the value block under a fixed kernel.

• FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices cs.LG · 2026-04-28 · unverdicted · none · ref 21

  Fed-FSTQ reduces uplink traffic by 46x and improves time-to-accuracy by 52% in federated LLM fine-tuning using Fisher-guided token quantization and selection.

• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification cs.LG · 2019-09-13 · unverdicted · none · ref 17

  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.

• Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings cs.LG · 2026-04-12 · unverdicted · none · ref 2

  Task2Vec-based unsupervised metrics of client embedding cohesion, dispersion, and density correlate strongly with final federated learning performance across multiple datasets and heterogeneity levels.

• A Survey on Foundation Models for Personalized Federated Intelligence cs.AI · 2025-05-11 · unverdicted · none · ref 141

  The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.

• Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning cs.LG · 2026-05-19 · unreviewed · ref 51