8 research outputs found
Concept-aware clustering for decentralized deep learning under temporal shift
Decentralized deep learning requires dealing with non-iid data across
clients, which may also change over time due to temporal shifts. While non-iid
data has been extensively studied in distributed settings, temporal shifts have
received no attention. To the best of our knowledge, we are first with tackling
the novel and challenging problem of decentralized learning with non-iid and
dynamic data. We propose a novel algorithm that can automatically discover and
adapt to the evolving concepts in the network, without any prior knowledge or
estimation of the number of concepts. We evaluate our algorithm on standard
benchmark datasets and demonstrate that it outperforms previous methods for
decentralized learning.Comment: 4 pages, 2 figure
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Limited data access is a longstanding barrier to data-driven research and
development in the networked systems community. In this work, we explore if and
how generative adversarial networks (GANs) can be used to incentivize data
sharing by enabling a generic framework for sharing synthetic datasets with
minimal expert knowledge. As a specific target, our focus in this paper is on
time series datasets with metadata (e.g., packet loss rate measurements with
corresponding ISPs). We identify key challenges of existing GAN approaches for
such workloads with respect to fidelity (e.g., long-term dependencies, complex
multidimensional relationships, mode collapse) and privacy (i.e., existing
guarantees are poorly understood and can sacrifice fidelity). To improve
fidelity, we design a custom workflow called DoppelGANger (DG) and demonstrate
that across diverse real-world datasets (e.g., bandwidth measurements, cluster
requests, web sessions) and use cases (e.g., structural characterization,
predictive modeling, algorithm comparison), DG achieves up to 43% better
fidelity than baseline models. Although we do not resolve the privacy problem
in this work, we identify fundamental challenges with both classical notions of
privacy and recent advances to improve the privacy properties of GANs, and
suggest a potential roadmap for addressing these challenges. By shedding light
on the promise and challenges, we hope our work can rekindle the conversation
on workflows for data sharing.Comment: Published in IMC 2020. 20 pages, 26 figure
Adaptive Expert Models for Personalization in Federated Learning
Federated Learning (FL) is a promising framework for distributed learning whendata is private and sensitive. However, the state-of-the-art solutions in thisframework are not optimal when data is heterogeneous and non-Independent andIdentically Distributed (non-IID). We propose a practical and robust approachto personalization in FL that adjusts to heterogeneous and non-IID data bybalancing exploration and exploitation of several global models. To achieve ouraim of personalization, we use a Mixture of Experts (MoE) that learns to groupclients that are similar to each other, while using the global models moreefficiently. We show that our approach achieves an accuracy up to 29.78 % andup to 4.38 % better compared to a local model in a pathological non-IIDsetting, even though we tune our approach in the IID setting.QC 20220628</p
Adaptive Expert Models for\ua0Federated Learning
Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting
Komplexanalytiska metoder inom talteori
Sammandrag
I det hÀr kandidatarbetet redogör vi för bevis av tre klassiska satser frÄn talteorin. Vi kommer
att bevisa primtalssatsen, tvÄ- och fyrkvadratssatsen och Dirichlets sats om primtal i aritmetiska
följder. Till vÄr hjÀlp tar vi begrepp ifrÄn komplexanalys och Fourieranalys, och arbetet innehÄller
dÀrför ocksÄ en grundlig teorigenomgÄng innan sjÀlva satserna kan bevisas.
Abstract
In this bachelor thesis we outline proofs for three classic theorems from number theory. We will
prove the prime number theorem, Jacobiâs two- and four-squares theorems and Dirichletâs theorem
on primes in arithmetic sequences. In proving these theorems, methods from complex analysis and
Fourier analys will be needed. Thus, this thesis includes a thorough review of the necessary theory