Provides tight convergence analyses for EF and EF21 error feedback algorithms in distributed optimization, recovering single-agent rates independently of agent count.
hub
Advances and open problems in federated learning
12 Pith papers cite this work. Polarity classification is still indexing.
12
Pith papers citing it
hub tools
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models performing best.
An analytical expected-gain score from calibrated posteriors and classwise reliability estimates decides escalation in VFL, improving communication-accuracy trade-off over baselines.
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
FLARE uses adaptive multi-dimensional reputation scores and soft exclusion to improve Byzantine robustness in federated learning by up to 16% over prior methods while handling a new Statistical Mimicry attack.
Compass decomposes multi-query multi-SLO planning for compound AI serving, exploits plan similarities, uses selective profiling, and applies bipartite matching at runtime to deliver 2.4-5.1x higher goodput and 3.8-4.5x lower costs.
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
LADSG is a unified defense framework that reduces success rates of passive, active, and direct label inference attacks in VFL by 30-60% via label anonymization, gradient substitution, and norm-based filtering.
BoBa uses data distribution inference and overlapping clustering with voting to detect backdoor attacks in non-IID federated learning, claiming attack success rates below 0.001.
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
Survey mapping LLM applications in software quality assurance to established standards including ISO/IEC 12207, ISO 25010, CMMI, and TMM, with case studies, challenges, and future directions.
citing papers explorer
-
Adaptive Federated Optimization
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.