Posters

Few-shot diffusion models efficiently generate customized images; this paper provides the first theoretical explanation, proving improved approximation and optimization bounds, escaping the curse of d…

Few-shot adversarial prompt learning significantly improves vision-language model robustness by learning adversarially correlated text supervision and a novel training objective that enhances multi-mo…

DCOD, a novel two-stage framework (Fetch & Forge), efficiently condenses object detection datasets, achieving comparable performance to full datasets at extremely low compression rates, significantly …

Ferrari, a novel federated feature unlearning framework, minimizes feature sensitivity via Lipschitz continuity, enabling effective and privacy-preserving data removal without full client participatio…

FERERO, a novel framework, tackles multi-objective learning by efficiently finding preference-guided Pareto solutions using flexible preference modeling and convergent algorithms.

This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation.

FEEL-SNN enhances spiking neural network robustness by mimicking biological visual attention and adaptive leak factors, resulting in improved resilience against noise and attacks.

Brain-inspired recurrent neural networks learn efficiently by using feedback control to approximate optimal gradients, enabling rapid movement corrections and efficient adaptation to persistent errors…

FedSSP tackles personalized federated graph learning challenges by sharing generic spectral knowledge and incorporating personalized preferences, achieving superior performance in cross-domain scenari…

FEDNE: a novel approach enabling collaborative dimensionality reduction of distributed data in federated learning without data sharing, achieved via surrogate loss functions and data augmentation.

FedLPA: One-shot federated learning with layer-wise posterior aggregation improves model accuracy in non-IID data by efficiently aggregating layer-wise posteriors of local models using a novel approac…

FedGTST significantly improves federated transfer learning by tuning cross-client statistics, achieving superior global transferability with minimal communication overhead.

FedGMKD: A novel federated learning framework uses knowledge distillation and discrepancy-aware aggregation for efficient, privacy-preserving personalized learning in heterogeneous data settings.

FedGMark: the first certified robust watermarking method for protecting Federated Graph Learning models against theft and unauthorized copying.

Federated Transformer (FeT) revolutionizes multi-party fuzzy vertical federated learning by encoding fuzzy identifiers and using a transformer architecture, achieving up to 46% accuracy improvement an…

This paper presents novel algorithms achieving speed-ups in differentially private federated online prediction from experts, addressing both stochastic and oblivious adversaries, with rigorous theoret…

This paper introduces federated natural policy gradient and actor-critic methods achieving near dimension-free global convergence for decentralized multi-task reinforcement learning, a significant bre…

FedMRL: a novel federated learning approach achieves high accuracy with low communication cost by enabling clients with heterogeneous models to collaboratively train using shared auxiliary models and …

Amplified SCAFFOLD: A new algorithm for federated learning significantly reduces communication rounds under periodic client participation and heterogeneous data, achieving linear speedup and resilienc…

Federated Learning over Connected Modes (FLOCO) accelerates global training and improves local accuracy in heterogeneous data settings by leveraging mode connectivity for collaborative model personali…