179 research outputs found
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Deep learning models are prone to forgetting information learned in the past
when trained on new data. This problem becomes even more pronounced in the
context of federated learning (FL), where data is decentralized and subject to
independent changes for each user. Continual Learning (CL) studies this
so-called \textit{catastrophic forgetting} phenomenon primarily in centralized
settings, where the learner has direct access to the complete training dataset.
However, applying CL techniques to FL is not straightforward due to privacy
concerns and resource limitations. This paper presents a framework for
federated class incremental learning that utilizes a generative model to
synthesize samples from past distributions instead of storing part of past
data. Then, clients can leverage the generative model to mitigate catastrophic
forgetting locally. The generative model is trained on the server using
data-free methods at the end of each task without requesting data from clients.
Therefore, it reduces the risk of data leakage as opposed to training it on the
client's private data. We demonstrate significant improvements for the
CIFAR-100 dataset compared to existing baselines
FairFed: Enabling Group Fairness in Federated Learning
As machine learning algorithms become increasingly integrated in crucial
decision-making scenarios, such as healthcare, recruitment, and risk
assessment, there have been increasing concerns about the privacy and fairness
of such systems. Federated learning has been viewed as a promising solution for
collaboratively training of machine learning models among multiple parties
while maintaining the privacy of their local data. However, federated learning
also poses new challenges in mitigating the potential bias against certain
populations (e.g., demographic groups), as this typically requires centralized
access to the sensitive information (e.g., race, gender) of each data point.
Motivated by the importance and challenges of group fairness in federated
learning, in this work, we propose FairFed, a novel algorithm to enhance group
fairness via a fairness-aware aggregation method, which aims to provide fair
model performance across different sensitive groups (e.g., racial, gender
groups) while maintaining high utility. This formulation can further provide
more flexibility in the customized local debiasing strategies for each client.
We build our FairFed algorithm around the secure aggregation protocol of
federated learning. When running federated training on widely investigated
fairness datasets, we demonstrate that our proposed method outperforms the
state-of-the-art fair federated learning frameworks under a high heterogeneous
sensitive attribute distribution. We also investigate the performance of
FairFed on naturally distributed real-life data collected from different
geographical locations or departments within an organization
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Semi-Automatic Query Expansion Approach to Web- Based Information Retrieval
The query used for Web searching is usually short and may not be able to reflect the intrinsic semantics of the user information need. The purpose of the paper is to take into account user information feedback, and to develop a semi-automatic query expansion approach to improve the effectiveness of Web searching. A search engine has been developed using the vector information retrieval model to validate the semi-automatic query expansion approach. The experiments show that this approach may improve the effectiveness of web searching
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