LEAF: A Benchmark for Federated Settings

1. SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity

   cs.LG 2026-07 conditional novelty 7.0 of 10

   Inter-client gradient divergence in federated learning concentrates in low-frequency components; suppressing them via spectral or spatial high-pass filtering reduces client drift and raises accuracy under non-IID data.

2. FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

   cs.LG 2026-07 conditional novelty 6.5 of 10

   Low-frequency components of client-side SAM perturbations carry most inter-client disagreement; high-pass filtering them yields more consistent federated updates and higher accuracy under non-IID data.

3. pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning

   cs.LG 2026-06 conditional novelty 6.5 of 10

   Layer-aware selective unlearning for personalized FL matches near-retrain forgetting while retaining ~97% personalized accuracy for remaining clients across four pFL architectures.

4. Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

   cs.LG 2026-08 conditional novelty 6.0 of 10

   LIGHTYEAR selects each client's aggregation set by scoring how similarly models behave on private validation data with a neural tangent kernel, improving robustness to heterogeneous and malfunctioning clients in peer-...

5. Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning

   cs.LG 2026-08 conditional novelty 6.0 of 10

   Update-based estimates of client data heterogeneity in sub-model federated learning are dominated by device capacity, and adaptive allocation adds nothing over a matched-budget random control once parameter coverage i...

6. AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning

   cs.DC 2026-07 conditional novelty 6.0 of 10

   An autoencoder-based split-learning compressor with a two-stage alignment protocol achieves about 10x communication reduction during pre-trained vision-model fine-tuning with near-zero accuracy loss, outperforming heu...

7. NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing

   cs.CR 2026-07 conditional novelty 6.0 of 10

   NFSA combines PRF-based two-layer secret sharing with CRT packing of almost key-homomorphic PRF masks to achieve single-server, one-shot secure aggregation without server-forwarded key shares.

8. PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

   cs.CR 2026-07 conditional novelty 6.0 of 10

   Multi-server multi-key FHE with a shared random mask lets PRoVeFL run complex Byzantine-robust FL aggregation privately and verifiably, with large reported speedups over Prio and ELSA.

9. Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces

   cs.LG 2025-09 conditional novelty 6.0 of 10

   Adaptive Sybil backdoor attacks can defeat MartFL, FLTrust, and SkyMask in buyer-baseline gradient marketplaces with little visible effect on accuracy or cost.

10. PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints

    cs.LG 2025-09 conditional novelty 6.0 of 10

    PracMHBench evaluates eight model-heterogeneous federated learning algorithms under practical edge device constraints and finds that depth-level heterogeneity wins under compute/communication limits while memory limit...

11. Differentially Private Federated Clustering with Random Rebalancing

    cs.LG 2025-08 reject novelty 6.0 of 10

    RR-Cluster enforces a minimum cluster size by random rebalancing, lowering DP noise and improving federated clustering utility, but its privacy proof understates the true noise.

12. Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources

    cs.DC 2025-07 conditional novelty 6.0 of 10

    Flotilla is a modular, resilient federated learning framework that runs on heterogeneous edge devices, supports sync and async strategies, and scales to 1000+ clients with low overhead.

13. Topology-Aware Differential Privacy in Hierarchical Federated Learning

    cs.CR 2025-06 conditional novelty 6.0 of 10

    The paper derives a per-client mutual-information bound for topology-aware leakage in differentially private federated learning and a min-max noise allocation that improves the bound over uniform noise when leverage s...

14. FedAPM: Federated Learning via ADMM with Partial Model Personalization

    cs.LG 2025-06 conditional novelty 6.0 of 10

    FedAPM applies ADMM with first- and second-order proximal corrections to partial model personalization in federated learning, proving global convergence and reporting better accuracy, F1, and AUC than FedAlt, FedSim, ...

15. HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A hierarchical asynchronous local SGD method with regional parameter servers and global model merging is claimed to train small LLMs up to 7.5x faster than DiLoCo in simulated geo-distributed settings.

16. HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark

    cs.LG 2025-06 conditional novelty 6.0 of 10

    HtFLlib is a unified benchmark and library with 12 datasets, 40 heterogeneous model architectures, and systematic accuracy, convergence, and cost evaluations of 10 HtFL methods.

17. DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems

    cs.LG 2025-06 conditional novelty 6.0 of 10

    DRAUN reconstructs unlearned client images from federated unlearning updates by simulating possible unlearning losses and matching gradients, exposing privacy leakage in optimization-based federated unlearning.

18. Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A federated learning method combines client-specific orthogonal transformations on frozen black-box foundation model embeddings with a shared classifier, outperforming baselines on several domain-shift benchmarks.

19. FedDuA: Doubly Adaptive Federated Learning

    cs.LG 2025-05 conditional novelty 6.0 of 10

    FedDuA sets each round's global learning rate to the average squared client-update norm divided by the aggregated update norm under an adaptive coordinate preconditioner, a rule that is minimax-optimal under an approx...

20. FNBench: Benchmarking Robust Federated Learning against Noisy Labels

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A unified benchmark of 18 federated learning methods under synthetic, human-annotation, and systematic label noise finds no consistently winning method and shows that an SVD-based decorrelation regularizer improves mo...

21. Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning

    cs.LG 2025-02 conditional novelty 6.0 of 10

    A skew-aware Byzantine attack, STRIKE, exploits the tendency of honest non-IID gradients to form dense clusters away from their mean, hiding malicious gradients inside the cluster.

22. Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Aequa allocates model widths (and thus accuracies) to federated learning participants in proportion to their estimated contributions, using slimmable networks and a simulated annealing optimizer.

23. Decoding FL Defenses: Systemization, Pitfalls, and Remedies

    cs.CR 2025-02 conditional novelty 6.0 of 10

    Many FL defenses are evaluated on overly easy datasets and attacks, and this paper demonstrates with case studies that those easy settings can make weak defenses look strong.

24. THOR: A Generic Energy Estimation Approach for On-Device Training

    cs.LG 2025-01 conditional novelty 6.0 of 10

    A layer-wise Gaussian Process model estimates DNN training energy from measured probe networks, reducing MAPE from about 40% to about 10% versus FLOPs-based estimation.

25. Personalized Language Model Learning on Text Data Without User Identifiers

    cs.LG 2025-01 reject novelty 6.0 of 10

    IDfree-PL samples user-specific embedding distributions on-device to personalize cloud language models without explicit user IDs, with theoretical and empirical support for accuracy gains and embedding-attribution resistance.

26. FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning

    cs.LG 2024-12 conditional novelty 6.0 of 10

    FedCFA replaces local latent factors with global average features to generate counterfactual samples, improving federated global model accuracy under heterogeneous data.

27. Incentivizing Truthful Collaboration in Heterogeneous Federated Learning

    cs.LG 2024-12 conditional novelty 6.0 of 10

    A norm-comparison payment rule makes truthful gradient reporting an approximately optimal strategy for clients in heterogeneous federated learning.

28. Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

    cs.LG 2024-11 conditional novelty 6.0 of 10

    A comprehensive survey that organizes non-IID data in federated learning into taxonomies of skew types, partition protocols, and metrics, with a meta-analysis of 235 selected papers.

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

    cs.LG 2026-06 conditional novelty 5.5 of 10

    HERO shows that FCL method rankings shift when client data skew and task-order mismatch are controlled separately, and that average accuracy can hide weak bottom-client performance.

30. Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs

    cs.LG 2026-08 conditional novelty 5.0 of 10

    Label-flipping and oversampling attacks on federated conditional GANs shift the generated target-class distribution toward the source class linearly in poisoning strength while only quadratically changing the true tar...

31. Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory

    cs.AI 2026-08 reject novelty 5.0 of 10

    In a lattice-based simulation of decentralized federated learning, a reputation mechanism that rewards cooperators and penalizes defectors raises average accuracy from 70% to 82% and drives cooperation to near 100%.

32. DFCA: Decentralized Federated Clustering Algorithm

    cs.LG 2025-10 conditional novelty 5.0 of 10

    DFCA decentralizes IFCA-style clustered federated learning: clients keep one model per cluster, train their assigned model locally, and exchange only that model with neighbors via a running average, matching centraliz...

33. FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks

    cs.LG 2025-08 conditional novelty 5.0 of 10

    FLAegis defends federated learning by SAX-transforming client updates, spectral-clustering them to filter malicious clients, and applying FFT-based robust aggregation, outperforming several baselines on FEMNIST.

34. FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A data-free GAN plus bidirectional knowledge distillation between global and local models improves both personalization and generalization in non-IID federated classification.

35. Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions

    cs.LG 2025-06 reject novelty 5.0 of 10

    A variance-reduction-based client selection with coalition clustering yields modest accuracy gains over baselines in heterogeneous federated learning, but its convergence guarantee rests on an assumption that the poli...

36. Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions

    cs.LG 2025-05 conditional novelty 5.0 of 10

    FLowDUP generates personalized federated models for unlabeled clients via a hypernetwork operating in a low-dimensional random subspace, with a transductive multi-task PAC-Bayes bound motivating the objective.

37. Topology-Aware Knowledge Propagation in Decentralized Learning

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Topology-aware aggregation using degree or betweenness centrality improves out-of-distribution knowledge propagation in decentralized learning across 36 simulated topologies and five datasets.

38. Chisme: Heterogeneity-Aware Gossip Learning

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Chisme uses cosine similarity between model updates to weight how much a gossip-learning client trusts incoming models, improving convergence and fairness under heterogeneous data.

39. FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing

    cs.LG 2025-05 conditional novelty 5.0 of 10

    A realistic 135-client federated learning benchmark for remote sensing, assembled from eight datasets, with baseline results for ten FL algorithms.

40. Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data

    cs.LG 2025-05 conditional novelty 5.0 of 10

    pFedLIA clusters federated learning clients using a cheap 'lazy influence' score, and on CIFAR10/100 and FashionMNIST it matches a perfect-clustering oracle while improving on existing personalized FL baselines by up ...

41. Incentivizing Inclusive Contributions in Model Sharing Markets

    cs.AI 2025-05 conditional novelty 5.0 of 10

    iPFL combines graph-based personalized federated learning with a game-theoretic payment rule, proving non-negative utility and one-sided truthfulness, and showing higher economic utility in simulated markets.

42. TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction

    cs.LG 2025-04 reject novelty 5.0 of 10

    TACO assigns client-specific correction coefficients based on gradient magnitude and direction to reduce over-correction in non-IID federated learning, improving accuracy, rounds, and wall-clock time.

43. PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

    cs.LG 2025-02 conditional novelty 5.0 of 10

    PLayer-FL picks the layer split in partial federated learning from a low-cost sensitivity metric computed at epoch 1, and reports competitive F1, fairness, and participation incentives across seven non-IID datasets.

44. Interaction-Aware Gaussian Weighting for Clustered Federated Learning

    cs.LG 2025-02 conditional novelty 5.0 of 10

    A loss-trajectory-based clustering method for federated learning, plus a Wasserstein-adjusted cluster metric, reports improved personalization on heterogeneous data.

45. Distributed Quasi-Newton Method for Fair and Fast Federated Learning

    cs.LG 2025-01 reject novelty 5.0 of 10

    DQN-Fed updates a global model in a direction that makes every client's loss decrease at a rate tied to its local quasi-Newton step, with claimed linear-quadratic convergence.

46. Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A clustering-based process for creating federated learning benchmarks with controllable semantic heterogeneity, demonstrated on panoptic scene graph generation and CelebA.

47. Distributed, communication-efficient, and differentially private estimation of KL divergence

    cs.LG 2024-11 reject novelty 5.0 of 10

    PRIEST-KLD is a family of differentially private, communication-efficient estimators of KL divergence for federated data, with three trust models; however, the unbiasedness and privacy proofs have load-bearing gaps.

48. Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning

    cs.LG 2024-11 conditional novelty 5.0 of 10

    Weak classes that are intrinsically confusable persist under class-balanced federated learning; a partial knowledge distillation method that distills from class-specific experts improves their accuracy.

49. Towards Effective Device-Aware Federated Learning

    cs.LG 2019-08 conditional novelty 5.0 of 10

    Federated learning aggregation can be improved by weighting clients with label diversity and model divergence, but tuning the priority order on test accuracy overstates the benefit.

50. Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data

    cs.LG 2026-07 conditional novelty 4.0 of 10

    FedAvg on non-IID KIIT-MiTA drone imagery recovers most centralized YOLO nano mAP while keeping images local, with YOLO26 nano gaining ~53% and ~68% relative mAP over single-drone baselines.

51. PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems

    cs.CR 2026-02 conditional novelty 4.0 of 10

    Combining federated learning with differential privacy causes catastrophic accuracy loss and large resource overhead in vision models, whereas federated learning with secure multi-party computation retains near-baseli...

52. FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning

    cs.LG 2025-08 reject novelty 4.0 of 10

    FedEve uses a Kalman filter to combine server momentum (prediction) with client updates (observation) to offset period drift and client drift in cross-device federated learning.

53. Adapting to Fragmented and Evolving Data: A Fisher Information Perspective

    cs.LG 2025-07 reject novelty 4.0 of 10

    FADE adapts to sequential covariate shift by combining a Fisher-weighted KL shift signal with a smoothed Fisher information regularizer, reporting gains on vision, text, and tabular benchmarks.

54. Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

    cs.DC 2025-07 conditional novelty 4.0 of 10

    A survey that builds a taxonomy of edge-cloud LLM-SLM collaboration for inference and training, claiming to be the first to unify both phases.

55. Federated Split Learning with Improved Communication and Storage Efficiency

    cs.LG 2025-07 conditional novelty 4.0 of 10

    CSE-FSL combines an auxiliary network for local updates with periodic smashed-data uploads and a single server-side model, claiming convergence under non-convex loss and lower communication and storage costs.

56. Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection

    cs.CR 2025-06 conditional novelty 4.0 of 10

    A federated FedAvg implementation of the CANdito LSTM autoencoder IDS achieves usable detection rates on the ReCAN dataset with slightly lower performance but higher communication cost than centralized training.

57. DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information

    cs.GT 2025-05 reject novelty 4.0 of 10

    A Bayesian-persuasion and dynamic-pricing mechanism for online federated learning under two-sided incomplete information, with a claimed 2ξ approximate optimality gap.

58. Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach

    cs.LG 2025-04 reject novelty 4.0 of 10

    The authors introduce IPSS, a stratified-sampling algorithm that approximates Shapley-based data value in federated learning by focusing on small, high-impact coalitions of clients.

59. Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning

    cs.DC 2024-12 conditional novelty 4.0 of 10

    A clustered federated learning framework uses specialized or ensemble models to pseudo-label unlabeled device data, with timing and scheduling heuristics that the authors say improve accuracy and cut energy use.

60. Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift

    cs.LG 2024-12 reject novelty 4.0 of 10

    FEDMPR, combining magnitude pruning, dropout, and noise injection in local training, reports accuracy gains over standard federated baselines on several image benchmarks, though not consistently in all settings.

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