343 research outputs found
Analyzing the dynamics between organizational culture and change : a case study of China Central Television (CCTV) in transition
The Thesis sets out to analyze CCTV's transition from 1979-2003 with a
special focus on its most influential reform entitled Producer Responsibility
System (PRS).
In order to present a real picture of CCTV's organizational culture, this research
uses multiple research methods to synthesize valuable contributions from two
schools of organizational culture theory driven by different research
orientations. Data collection methods include a6 months' ethnographic
research project inside CCTV.
The research has two main research findings. First, following the introduction
of PRS, the reform process has been uneven. A split has emerged at CCTV
between an 'inner' and an 'outer' management circles, with very different
organizational cultures and responses to organizational change. Second, the
research identifies four logics which have shaped CCTV's organizational
culture: Party logic, Commercial logic, Professional logic and Social and ethnic
logic. CCTV's transition has been defined by a complex interaction and
negotiation between these four logics.
This thesis summarizes CCTV's organizational change from 1979-2003 into
three stages, from a 'frozen' status to 'change by exception' and then to
'incremental change'. Analysis of the relationship between these four logics
suggests that to achieve a real transition from Party mouthpiece to modem
media enterprise, CCTV needs to achieve a new 'paradigm change'. The key to
the success of this 'paradigm change' will be a systematic reconstruction of
CCTV's organizational culture based on the central objective of building media
professionalism.
The single case study places some limits on the generalizability of the findings
but other Chinese media businesses share a similar economic, historical and
cultural context. The problems at CCTV can thus be seen to be representative
general issues of the Chinese media industry in transition
Adaptive Federated Learning via Entropy Approach
Federated Learning (FL) has recently emerged as a popular framework, which
allows resource-constrained discrete clients to cooperatively learn the global
model under the orchestration of a central server while storing
privacy-sensitive data locally. However, due to the difference in equipment and
data divergence of heterogeneous clients, there will be parameter deviation
between local models, resulting in a slow convergence rate and a reduction of
the accuracy of the global model. The current FL algorithms use the static
client learning strategy pervasively and can not adapt to the dynamic training
parameters of different clients. In this paper, by considering the deviation
between different local model parameters, we propose an adaptive learning rate
scheme for each client based on entropy theory to alleviate the deviation
between heterogeneous clients and achieve fast convergence of the global model.
It's difficult to design the optimal dynamic learning rate for each client as
the local information of other clients is unknown, especially during the local
training epochs without communications between local clients and the central
server. To enable a decentralized learning rate design for each client, we
first introduce mean-field schemes to estimate the terms related to other
clients' local model parameters. Then the decentralized adaptive learning rate
for each client is obtained in closed form by constructing the Hamilton
equation. Moreover, we prove that there exist fixed point solutions for the
mean-field estimators, and an algorithm is proposed to obtain them. Finally,
extensive experimental results on real datasets show that our algorithm can
effectively eliminate the deviation between local model parameters compared to
other recent FL algorithms.Comment: 7 pages, 4 figure
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