GreenGNN: Energy-Aware Windowed Communication Optimization for Distributed GNN Training
2026-06-03T08:52:24Z•b24f95dbd6feac1d3d45a5bb8286a1df05ce79703c9d6a3d24d241f253a2e63d
Byzantine-consensusGNNHNSWKafka-StreamsLLM-servingRPC-amortizationapproximate-nearest-neighborbitmap-representationblockchaincachingcloud-cost-optimizationdata-deduplicationdata-integrity risks','attack-surface','DoS-resource-exhaustion'distributed-systemsedge-computingenergy-efficiencyfuzzy-deduplicationgraph-neural-networksgraph-partitioningmodel-parallelismobject-storageprivacyshufflestream-processingunauthenticated-BFT
What happened
Collection of arXiv system and ML papers (2026-06-03) covering distributed GNN training (GreenGNN), online fuzzy deduplication with incremental HNSW (FOLD), object-storage-backed stream repartitioning for Kafka Streams (BlobShuffle), streaming multi-objective graph partitioning (SIGMA), unauthenticated Byzantine consensus latency optimization (Fast TetraBFT), edge/fog LLM serving (E2LLM), lakehouse query-runtime variance, a generative Markov model for distributed systems, a GPU field-line library (Streami), and a constant-size recurrent memory for robot policies (AURA). Key technical advances:
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- b24f95dbd6feac1d3d45a5bb8286a1df05ce79703c9d6a3d24d241f253a2e63d
- Enrichment time
- 2026-06-03T08:52:24Z
- AI-assisted enrichment
- Yes
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