Real 5G testbed experiments show consistent stragglers in 70% of federated learning trials due to communication delays, challenging common wireless FL assumptions.
2025.3527641
5 Pith papers cite this work. Polarity classification is still indexing.
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PACT reduces benign-normalized false-positive burden by 43% and 21% on AIT-ADS and BOTSv1 benchmarks versus a frozen baseline while issuing 3.8x–5.2x fewer analyst queries than random updating.
Simulations show information overload decreases source localization effectiveness in networks, with Erdős-Rényi graphs more resilient than Barabási-Albert ones and a reversal where less dense networks perform better under strong overload.
ZipMoE delivers up to 72.77% lower inference latency and 6.76x higher throughput for on-device MoE models via lossless compression and cache-affinity scheduling with a claimed provable guarantee.
The authors provide a systematization of differentially private graph release methods along with an objective-based framework and two illustrative evaluations for social network analysts.
citing papers explorer
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Beyond Assumptions: Measuring Federated Learning over Real 5G Networks
Real 5G testbed experiments show consistent stragglers in 70% of federated learning trials due to communication delays, challenging common wireless FL assumptions.
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PACT: Reducing Alert Fatigue in Low-Prevalence SOC Streams with Triggered Active Learning
PACT reduces benign-normalized false-positive burden by 43% and 21% on AIT-ADS and BOTSv1 benchmarks versus a frozen baseline while issuing 3.8x–5.2x fewer analyst queries than random updating.
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Nonlinear dynamics of information overload: Impact on source localization in complex networks
Simulations show information overload decreases source localization effectiveness in networks, with Erdős-Rényi graphs more resilient than Barabási-Albert ones and a reversal where less dense networks perform better under strong overload.
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ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling
ZipMoE delivers up to 72.77% lower inference latency and 6.76x higher throughput for on-device MoE models via lossless compression and cache-affinity scheduling with a claimed provable guarantee.
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SoK: Practical Aspects of Releasing Differentially Private Graphs
The authors provide a systematization of differentially private graph release methods along with an objective-based framework and two illustrative evaluations for social network analysts.