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shards scholar.arxiv · encyclopedia.wikipedia
local mesh 8 on-topic claims
queued local mesh: 8 on-topic claims (relevance gated) packed DEAI_RESEARCH_v1 JobSubmit blob (1243 bytes, cid deai-research-named-shard-v1) named shards: scholar.arxiv, encyclopedia.wikipedia research shards scholar.arxiv,encyclopedia.wikipedia JobSealed opened; grafting claim pack searching scholar.arxiv… scholar.arxiv 4 hits searching encyclopedia.wikipedia… encyclopedia.wikipedia 4 hits reused 0 cids, new 3 on-topic sources, extracted 9 claims, grafted 2 links; dropped 5 off-topic hits and 0 off-topic local claims (VAFL: a Method of Vertical Asynchronous Federated Learning; Efficient Vertical Federated Learning with Secure Aggregation; Local differential privacy; Federated Learning of Cohorts; SimHash) sealed review held · labels 12 · map links 2 library artifacts minted
reused 0 · new sources 3 · new claims 9 · new links 2
signature 8a51e3dd7e8f931bb7d2f5efb7e51dce5a1a1873bb931eb735eeeaea558849e2be68f0a711b40db75a46d788c18f52f99d9f804c43f46587613c1ffcfa7c800d
In this work, we propose a distributed optimization algorithm, Distributed Low-Communication (DiLoCo), that enables training of language models on islands of devices that are poorly connected. — DiLoCo: Distributed Low-Communication Training of Language Models (2023)
OpenDiLoCo is an open-source implementation and replication of the Distributed Low-Communication (DiLoCo) training method for large language models. — OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training (2024)
However, existing secure aggregation protocols incur high computation/communication costs, especially when the number of model parameters is larger than the number of clients participating in an iteration -- a typical scenario in federated learning. — FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning (2020)
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. — Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications (2024)
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