Fair Differentially Private Federated Learning Framework

Federated learning (FL) is a distributed machine learning strategy that enables participants to collaborate and train a shared model without sharing their individual datasets. Privacy and fairness are crucial considerations in FL. While FL promotes privacy by minimizing the amount of user data stored on central servers, it still poses privacy risks that need to be addressed. Industry standards such as differential privacy, secure multi-party computation, homomorphic encryption, and secure aggregation protocols are followed to ensure privacy in FL. Fairness is also a critical issue in FL, as models can inherit biases present in local datasets, leading to unfair predictions. Balancing privacy and fairness in FL is a challenge, as privacy requires protecting user data while fairness requires representative training data. This paper presents a "Fair Differentially Private Federated Learning Framework" that addresses the challenges of generating a fair global model without validation data and creating a globally private differential model. The framework employs clipping techniques for biased model updates and Gaussian mechanisms for differential privacy. The paper also reviews related works on privacy and fairness in FL, highlighting recent advancements and approaches to mitigate bias and ensure privacy. Achieving privacy and fairness in FL requires careful consideration of specific contexts and requirements, taking into account the latest developments in industry standards and techniques.Comment: Paper report for WASP module

SYNTHESIS, COMPUTER AIDED SCREENING AND PHARMACOLOGICAL EVALUATION OF 2/3-SUBSTITUTED-6(4-METHYLPHENYL)-4,5-DIHYDROPYRIDAZIN3(2H)-ONES, AND PYRIDAZINE SUBSTITUTED TRIAZINE

The present research work involved synthesis of some novel pyridazine derivatives and evaluation of their analgesic and anti-inflammatory activities in experimental animals to obtain safer non-steroidal anti-inflammatory drugs (NSAIDs). Friedal craft acylation reaction of succinic anhydride with toluene in the presence of anhydrous aluminum chloride gave 4-(4-methylphenyl)-4-oxo-butanoic acid (1). The aryl propionic acid 1 on reaction with phenyl hydrazine and hydrazine hydrate yielded the pyridazinone derivative 2 and 3, respectively. Reaction of the compound 3 with phosphorus oxychloride (POCl3) produced the corresponding chloropyridazine derivative 4. A 4-hydroxymethyl derivative of dihydropyridazinone (5) was synthesized by condensing 3 with methanol and formaldehyde (HCHO). The compound 5 on further treatment with guanidine hydrochloride in ethanol gave the pyridazino-triazine (6). The synthesized compounds were investigated for their analgesic activity in mice and anti-inflammatory activity in Wistar albino rats. The molecular, pharmacokinetic and toxicity properties of the synthesized compounds were calculated by Molinspiration and Osiris property explorer software. The results of in-vivo anti-inflammatory studies revealed that the compound. 4 showed maximum inhibition in paw edema volume followed by compound no. 3 while the compound no. 4 exhibited excellent  peripheral analgesic activity (74%) followed by the compound no. 5. Compound no. 4 and 5 also showed good central analgesic effect increased the reaction time to 90 minutes. All the title compounds except compound 5 are predicted to be safe by Osiris online software and are likely to have good oral bioavailability as they obey Lipinski’s rule of five for drug likeness