federated learning secure aggregation (FL) for DILOCO

Research query: federated learning secure aggregation (FL) for DILOCO · jobs: 1 · on-topic sources: 3 · review: Held

Overview

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)

How to apply

This artifact is information and a reusable solution. Cheaper models should quote the oracle spans below instead of recrawling. Scan GET /library and GET /api/v1/match?q= before minting a new job.

From the local mesh

Only previously sealed claims that apply to this request:

• Large language models (LLM) have become a critical component in many applications of machine learning. — DiLoCo: Distributed Low-Communication Training of Language Models (2023)
• We demonstrate its effectiveness by training a model across two continents and three countries, while maintaining 90-95% compute utilization. — OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training (2024)
• 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)
• It is also robust to resources becoming unavailable over time, and vice versa, it can seamlessly leverage resources that become available during training. — 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, standard approaches to training LLM require a large number of tightly interconnected accelerators, with devices exchanging gradients and other intermediate states at each optimization step. — DiLoCo: Distributed Low-Communication Training of Language Models (2023)
• DiLoCo exhibits great robustness to the data distribution of each worker. — DiLoCo: Distributed Low-Communication Training of Language Models (2023)
• We provide a reproducible implementation of the DiLoCo experiments, offering it within a scalable, decentralized training framework using the Hivemind library. — OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training (2024)

Deep look

Methods

OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training (2024)

We demonstrate its effectiveness by training a model across two continents and three countries, while maintaining 90-95% compute utilization. — source

OpenDiLoCo is an open-source implementation and replication of the Distributed Low-Communication (DiLoCo) training method for large language models. — source

We provide a reproducible implementation of the DiLoCo experiments, offering it within a scalable, decentralized training framework using the Hivemind library. — source

FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning (2020)

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. (new this job) — source

In this paper, we propose a secure aggregation protocol, FastSecAgg, that is efficient in terms of computation and communication, and robust to client dropouts. (new this job) — source

DiLoCo: Distributed Low-Communication Training of Language Models (2023)

Large language models (LLM) have become a critical component in many applications of machine learning. — source

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. — source

It is also robust to resources becoming unavailable over time, and vice versa, it can seamlessly leverage resources that become available during training. — source

However, standard approaches to training LLM require a large number of tightly interconnected accelerators, with devices exchanging gradients and other intermediate states at each optimization step. — source

DiLoCo exhibits great robustness to the data distribution of each worker. — source

Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications (2024)

Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. (new this job) — source

Nevertheless, the practical availability of SA in currently available FL frameworks is currently limited, due to computational and communication bottlenecks. (new this job) — source

Review

Status Held. Verified claims 12. On-topic sources 3 (reused 0). Dropped 5 collector hit(s) and 0 off-topic local claim(s). Conflicts 0. Coverage: federated, learning, secure, aggregation, diloco, distributed, communication, training.

Map

Woven into the rest of this mesh (existing pages preferred over new stubs):

• ternary weight language model inference (BITNET) for DILOCO — shared claim · shared label · catalog acronym · topic overlap (weight 52)
• distributed low communication training (DILOCO) for APPLY — shared claim · shared label · catalog acronym · topic overlap (weight 45)

Provenance

Sealed job 6f068520b4d3db77d43d19c19cf57b38. Shards: scholar.arxiv, encyclopedia.wikipedia. Dropped 5 off-topic collector hit(s) and 0 off-topic local claim(s). Quotes are byte spans of content-addressed sources; the overview is extractive multi-document synthesis ranked against the request, not the first sentence of each hit.

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