federated learning
for ml engineers, mobile network teams
Why this scored 6
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
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HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
Uses SDN-orchestrated client partitioning to improve hybrid federated learning efficiency. · for ml engineers, network architects
arxivSteadyai22d ago - 217
Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport
Highlights bandwidth and privacy challenges when training LLMs across mobile RAN networks. · for ml engineers, mobile network teams
arxivSteadyai2h ago