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Colbertv2: Effective and efficient retrieval via lightweight late interaction
K Santhanam, O Khattab, J Saad-Falcon… - arxiv preprint arxiv …, 2021 - arxiv.org
Neural information retrieval (IR) has greatly advanced search and other knowledge-
intensive language tasks. While many neural IR methods encode queries and documents …
intensive language tasks. While many neural IR methods encode queries and documents …
FedNL: Making Newton-type methods applicable to federated learning
M Safaryan, R Islamov, X Qian, P Richtárik - arxiv preprint arxiv …, 2021 - arxiv.org
Inspired by recent work of Islamov et al (2021), we propose a family of Federated Newton
Learn (FedNL) methods, which we believe is a marked step in the direction of making …
Learn (FedNL) methods, which we believe is a marked step in the direction of making …
Progfed: effective, communication, and computation efficient federated learning by progressive training
Federated learning is a powerful distributed learning scheme that allows numerous edge
devices to collaboratively train a model without sharing their data. However, training is …
devices to collaboratively train a model without sharing their data. However, training is …
Linearly converging error compensated SGD
E Gorbunov, D Kovalev… - Advances in Neural …, 2020 - proceedings.neurips.cc
In this paper, we propose a unified analysis of variants of distributed SGD with arbitrary
compressions and delayed updates. Our framework is general enough to cover different …
compressions and delayed updates. Our framework is general enough to cover different …
EF21-P and friends: Improved theoretical communication complexity for distributed optimization with bidirectional compression
K Gruntkowska, A Tyurin… - … Conference on Machine …, 2023 - proceedings.mlr.press
In this work we focus our attention on distributed optimization problems in the context where
the communication time between the server and the workers is non-negligible. We obtain …
the communication time between the server and the workers is non-negligible. We obtain …
Federated learning via synthetic data
Federated learning allows for the training of a model using data on multiple clients without
the clients transmitting that raw data. However the standard method is to transmit model …
the clients transmitting that raw data. However the standard method is to transmit model …
DoCoFL: Downlink compression for cross-device federated learning
R Dorfman, S Vargaftik… - … on Machine Learning, 2023 - proceedings.mlr.press
Many compression techniques have been proposed to reduce the communication overhead
of Federated Learning training procedures. However, these are typically designed for …
of Federated Learning training procedures. However, these are typically designed for …
Analysis of error feedback in federated non-convex optimization with biased compression: Fast convergence and partial participation
In practical federated learning (FL) systems, the communication cost between the clients and
the central server can often be a bottleneck. In this paper, we focus on biased gradient …
the central server can often be a bottleneck. In this paper, we focus on biased gradient …
A compressed gradient tracking method for decentralized optimization with linear convergence
Communication compression techniques are of growing interests for solving the
decentralized optimization problem under limited communication, where the global objective …
decentralized optimization problem under limited communication, where the global objective …
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees
C Philippenko, A Dieuleveut - arxiv preprint arxiv:2006.14591, 2020 - arxiv.org
We introduce a framework-Artemis-to tackle the problem of learning in a distributed or
federated setting with communication constraints and device partial participation. Several …
federated setting with communication constraints and device partial participation. Several …