{"total":24,"rows":[{"id":"2207.10221","title":"Slimmable Quantum Federated Learning","created":"2022-07-20","cited_by_count":23,"area":null},{"id":"2203.08875","title":"SC2 Benchmark: Supervised Compression for Split Computing","created":"2022-03-16","cited_by_count":15,"area":null},{"id":"2311.05739","title":"Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency","created":"2023-11-09","cited_by_count":8,"area":null},{"id":"2411.18199","title":"Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges","created":"2024-11-27","cited_by_count":5,"area":null},{"id":"2511.18151","title":"AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems","created":"2025-11-22","cited_by_count":4,"area":null},{"id":"2303.12524","title":"Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications","created":"2023-03-22","cited_by_count":3,"area":null},{"id":"2403.17154","title":"On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance","created":"2024-03-25","cited_by_count":3,"area":null},{"id":"2309.17401","title":"Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing","created":"2023-09-29","cited_by_count":2,"area":null},{"id":"2403.00942","title":"Resilience of Entropy Model in Distributed Neural Networks","created":"2024-03-01","cited_by_count":1,"area":null},{"id":"2405.19669","title":"Texture-guided Coding for Deep Features","created":"2024-05-30","cited_by_count":1,"area":null},{"id":"2406.13086","title":"NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation","created":"2024-06-18","cited_by_count":1,"area":null},{"id":"2411.00907","title":"On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance","created":"2024-11-01","cited_by_count":1,"area":null},{"id":"2510.16747","title":"An Efficient Semantic Segmentation Decoder for In-Car or Distributed Applications","created":"2025-10-19","cited_by_count":1,"area":null},{"id":"2511.02293","title":"3D Point Cloud Object Detection on Edge Devices for Split Computing","created":"2025-11-04","cited_by_count":1,"area":null},{"id":"2511.11608","title":"Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2607.17913","title":"AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning","created":"2026-07-20","cited_by_count":1,"area":null},{"id":"2402.10021","title":"SAWEC: Sensing-Assisted Wireless Edge Computing","created":"2024-02-15","cited_by_count":0,"area":null},{"id":"2412.16616","title":"Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach","created":"2024-12-21","cited_by_count":0,"area":null},{"id":"2511.13779","title":"Semantic Multiplexing","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2601.07119","title":"SC-MII: Infrastructure LiDAR-based 3D Object Detection on Edge Devices for Split Computing with Multiple Intermediate Outputs Integration","created":"2026-01-12","cited_by_count":0,"area":null},{"id":"2603.05172","title":"Trainable Bitwise Soft Quantization for Input Feature Compression","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2608.28684","title":"Distributed Semantic Segmentation With Improved Rate-Distortion Trade-Off","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.14660","title":"MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks","created":"2026-09-13","cited_by_count":0,"area":null},{"id":"2609.30629","title":"FRESHLATENT: Channel-Aware Latent Adaptation for Resource-Constrained Embodied VLM Perception","created":"2026-09-24","cited_by_count":0,"area":null}],"next_cursor":null}