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1,995 papers
Clustered Federated Learning (FL) partitions a client population into groups of similar local distributions and trains one specialized model per cluster, mitigating client drift that degrades single-model methods under non-IID data. Prior methods discover cluster structure inside the training loop through gradient simi...
World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use distributed records, while ownership boundaries within a trajectory restrict the examples each client can construct. Our study benchmarks this cross-time setting through hou...
We consider a distributed learning task with agents that have correlated data. Specifically, the label of an agent depends on the input of other agents for the same sample, and these inputs are also correlated. Correlated data is the reality when agents share the same environment. Existing decentralized methods, such a...
Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with recurrent state-space dynamics. However, the behavior of SSMs in distributed learning settings remains poorly understood. In particular, the existing standard federated lear...
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Theranostics links disease characterization with treatment selection and response assessment, creating a framework in which diagnostic information directly informs individualized therapy. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning, radiomics, radiogenomics, and multimodal data...
Engineering colleges across India face a critical challenge, with approximately 28% of enrolled students failing to complete their degrees on time. In regions like Vidarbha, Maharashtra, this rate is exacerbated by localized financial stress, first-generation academic backgrounds, and constrained support systems. Tradi...
The increasing distribution of enterprise computing across cloud platforms, edge devices, branch networks, and Internet-of-Things environments has made the collection and centralized analysis of network telemetry increasingly difficult. Conventional deep-learning intrusion detection approaches commonly require the move...
Federated learning, which enables the training of intelligent models without transferring data and solely by relying on the computational capabilities of distributed devices, faces significant challenges in few-shot scenarios due to data heterogeneity, resource constraints, and slow convergence. The primary obstacle li...
Engineering colleges across India face a critical challenge, with approximately 28% of enrolled students failing to complete their degrees on time. In regions like Vidarbha, Maharashtra, this rate is exacerbated by localized financial stress, first-generation academic backgrounds, and constrained support systems. Tradi...
Theranostics links disease characterization with treatment selection and response assessment, creating a framework in which diagnostic information directly informs individualized therapy. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning, radiomics, radiogenomics, and multimodal data...
The increasing distribution of enterprise computing across cloud platforms, edge devices, branch networks, and Internet-of-Things environments has made the collection and centralized analysis of network telemetry increasingly difficult. Conventional deep-learning intrusion detection approaches commonly require the move...
FedDWM: A Dynamic Watermark Migration Framework for Model Poisoning Defense in Federated Learning
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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