Introduces staleness-bucket aggregation with padding for distributed perceptron and proves finite-horizon mistake bounds where delay affects only mean staleness and noise adds a sqrt(horizon) term.
Federated learning via over- the-air computation,
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Analog RF computing performs neural network matrix-vector multiplications via RF waveform mixing at clients in MU-MIMO systems, reducing energy consumption by nearly two orders of magnitude compared to digital computing.
Hierarchical federated learning should be treated as an architecture-aware design framework for networked AI in which convergence depends on chosen hierarchy depth, layer-wise optimization roles, and communication realizations.
A structured survey of edge perception that integrates sensing modalities, edge AI, task-driven designs, and open challenges for 6G networks.
citing papers explorer
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Distributed Perceptron under Bounded Staleness, Partial Participation, and Noisy Communication
Introduces staleness-bucket aggregation with padding for distributed perceptron and proves finite-horizon mistake bounds where delay affects only mean staleness and noise adds a sqrt(horizon) term.
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Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems
Analog RF computing performs neural network matrix-vector multiplications via RF waveform mixing at clients in MU-MIMO systems, reducing energy consumption by nearly two orders of magnitude compared to digital computing.
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Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Hierarchical federated learning should be treated as an architecture-aware design framework for networked AI in which convergence depends on chosen hierarchy depth, layer-wise optimization roles, and communication realizations.
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Sense Smarter, Think Better: Edge Perception for Next-Generation Networks
A structured survey of edge perception that integrates sensing modalities, edge AI, task-driven designs, and open challenges for 6G networks.